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\title{Multimodal Biometrics Enhancement Recognition System based on Fusion of Fingerprint and PalmPrint: A Review}
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             \author[1]{Mouad.  M.H.Ali}

             \affil[1]{  Dr. Babasaheb Ambedkar Marathwada University}

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\date{\small \em Received: 10 December 2015 Accepted: 1 January 2016 Published: 15 January 2016}

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\begin{abstract}
        


This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolment & recognition the biometrics data. Secondly, the pre-processing stage which includes the enhancement and segmentation of Region-Of-Interest ROI. Thirdly, features extracted from the output of the preprocessing and each modal of biometrics having different type of features. Fourthly, the matching stage is to compare the acquired feature with the template in the database. Finally, the database which stores the features for the matching stags. Multimodal is being gathered of various types of biometrics objects from the same human. In this paper, the biometric system gives an explanation for each model. Also, the modalities of biometrics are discussed as well as focused on two different modalities: fingerprint and Palm-Print.

\end{abstract}


\keywords{multimodal, ROI, fingerprint and palm-print, fusion.}

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\let\tabcellsep& 	 	 		 
\section[{I. Introduction}]{I. Introduction}\par
long of various biometrics techniques , In the past few decades, human-beings have been addicted to various technologies such as captured photos, scanned signatures, bar code systems, verification Id \& so on. Also, Biometrics is one of the applications in Image processing. Biometrics refers to technologies that measure and analyze human body characteristics for the user authentication. The biometric authentication system based on two modes: Enrolment and Recognition. In the enrolment mode, the biometric data is acquired from the sensor and stored in a database along with the person's identity for the recognition. In the recognition mode, the biometric data is re-acquired from the sensor and compared to the stored data to determine the user identity. Biometric recognition based on uniqueness and permanence. The uniqueness means that there is no similarity of feature between two different biometrics data. For example, there are no two humans having the same fingerprint feature even if they are twins. And when the features of biometrics do not change over the lifetime or aging, it is called permanence. Biometrics can have physiological or behavioural characteristics. The physiological characteristics are included in the physical part of body such as (fingerprint, palm print, iris, face, DNA, hand geometry, retina... etc). The behavioral characteristics are based on an action taken by a person such as (Voice recognition, keystroke-scan, and signature-scan). 
\section[{II. Biometric Modalities a) Fingerprint}]{II. Biometric Modalities a) Fingerprint}\par
The fingertip surface consists of ridges and valleys. The ridge declare as black lines and the valleys declare as white lines Fig. \hyperref[fig_0]{1} .The minutiae points are the points where the ridge structure changes such as bifurcation and end point The human palm means the inner area between the fingers and wrist. The area of palm print compared to fingerprint is much larger, and then it can extract more features than a fingerprint. The palm print is similar to the fingerprint in ridges and valleys but the palm has also principle lines and wrinkles which can be acquired with a lower resolution scanner. 
\section[{c) Face}]{c) Face}\par
Face recognition is the popular way for the humans to recognize each other. The face is the front part of a head from chin to the forehead. Face recognition can be used in surveillance application because the face is one of the few biometric traits that can be recognized by people at distance \hyperref[b0]{[1]}. 
\section[{d) Iris}]{d) Iris}\par
Iris means a ring-shaped behind the cornea of the eye. The iris is very difficult to use after death because it's one of the first parts of the body to decay after death. Also the right iris is different from the left iris. 
\section[{A e) Retina}]{A e) Retina}\par
Retina is the layer of blood vessels which is located on the back of the eye. It is one of most secure in Biometrics because it is not easy to change or replicate the retinal vasculature. 
\section[{f) Hand geometry}]{f) Hand geometry}\par
Hand geometry recognition measures the size and shape of palm, and length and width of fingers. The merits are easy to use, technique is very simple. The demerit of hand geometry is that it can't be embedded to small devices like laptops, because the hand geometry sensor is large. Therefore, the hand geometry is suitable for verification only. 
\section[{g) Voice}]{g) Voice}\par
Voice Recognition is the task of recognizing people from their voices. It is a combination of behavior and physical biometrics. The physical features of voice are vocal tracts, mouth, nasal cavities, and lips which used to create the voices h) Gait Gait is the way of walking. Gait Biometrics can be used in surveillance application because it can be recognised at a distance. 
\section[{i) Signature}]{i) Signature}\par
Signature is a type of behavior biometrics and it can be changed by the person. The biometric system identifies the signature from the way of holding the pen and the time taken to sign. Also, it can be online or offline. 
\section[{j) Keystroke}]{j) Keystroke}\par
Keystroke is the way of typing on the keyboard. Most people have different ways to deal with the keyboard but this type of biometrics cannot be based for security accessing, thus it can be used after a strong biometrics for verification only 1:1. k) DNA DNA refers to deoxyribonucleic acid. This type of biometric is used in crime investigation. The identical twins have the same DNA pattern. 
\section[{III.}]{III.} 
\section[{Fingerprint}]{Fingerprint}\par
Fingerprints are graphical patterns of ridges and valleys on the surface of fingertips , the ridge ending and ridge bifurcation is called minutiae as shown in fig. \hyperref[fig_1]{2}. There are many methods based on minutiaebased fingerprint representation were proposed in \hyperref[b0]{[1]}, \hyperref[b1]{[2]} . Every person has a unique fingerprint from any other person. The fingerprint identification is based on two basic assumptions:-Invariance and Singularity Invariance : means the fingerprint characteristics do not change along the life. Singularity: means the fingerprint is unique and no two persons have the same pattern of fingerprint. The angle between the horizontal and the direction of the ridge. 6\par
Bifurcation Angle\par
The angle between the horizontal and the direction of the valley ending between the bifurcations. 7\par
Matching Score it is used to calculate the matching score between the input and template data 8\par
False Non Matching Ratio It is the probability that the system denies access to an approved user.\par
The main stages of fingerprint recognition system are shown in fig.  The Image Acquisition stage is the process to obtain images by different ways. There are two ways to capture fingerprint image; online and offline. In the online fingerprint identification the optical fingerprint reader is used to capture the image of fingerprint. The size of fingerprint image will be 260*300 pixels. The offline fingerprint identification is obtained by ink in the area of finger and then put a sheet of white paper on the fingerprint and finally scans the paper to get a digital image. 
\section[{b) Image Pre-processing Stage}]{b) Image Pre-processing Stage}\par
The pre-processing stage is the process of removing unwanted data in the fingerprint image such as noise, reflection.etc. The fingerprint image preprocessing is used to increase the clarity of ridge structure. There are many steps for doing this process such as Image Segmentation, Binarization, Elimination of noise ,smoothing and thinning. The propose of all these steps is to enhanced fingerprint image at the time of enrolment. In \hyperref[b2]{[3]},in addition to Gaussian filter, Short Time Fourier Transform (STFT) analysis is adopted to enhance fingerprint image quality. Sometimes the binarized fingerprint image contains a number of false minutiae. In \hyperref[b3]{[4]}.a detailed pre -processing is mentioned to remove false minutiae. Jiao Ruili et. al., \hyperref[b4]{[5]} proposed an automatic fingerprint acquisition and pre-processing system with a fixed point DSP, TMS320VC5509A and a fingerprint sensor, MBF200. The system is diminutive and flexible. The author presents a VC5509A based fingerprint pre-processing system, accomplished fingerprint image acquisition. The pre-processing system is accomplished with the properly selected algorithm on a DSP platform. Comparing the results of the algorithms, appropriate algorithms are selected for fingerprint identification pre-processing. They are Median Filtering, Directional Filtering Enhancement, Fixed Threshold Binarization, and Hilditch Thinning. Yun and Cho \hyperref[b5]{[6]} proposed an adaptive pre-processing method, which extracts five features from the fingerprint images, analyses image quality with clustering method, and enhances the images according to their characteristics. The pre-processing is performed after distinguishing the fingerprint image quality according to its characteristics. The Table show the some recent research of pre-processing. The feature extraction process of fingerprint image applied on the output of pre-processing stage. The process of feature extraction depends on set of algorithms; A fingerprint feature extraction program is to locate, measure and encode ridge endings and bifurcations in the fingerprint. For extracting the features from the fingerprint image, a popular method is minutiae extraction. Minutiae extraction algorithm will find out the minute points from the fingerprint and then map their relative placement on the finger.\par
There are two types of minutiae points: Ridge ending and Ridge bifurcation \hyperref[b6]{[7]}. In \hyperref[b7]{[8]} an advanced fingerprint feature extraction method is introduced through which minutiae are extracted directly from original gray-level fingerprint images without binarization and thinning. Gabor filter bank can also be used to extract features from fingerprint \hyperref[b8]{[9]}. Afsar et. al., \hyperref[b9]{[10]} presented the minutiae based Automatic Fingerprint Identification Systems. The technique is based on the extraction of minutiae from the thinned, binarized and segmented version of a fingerprint image. The system uses fingerprint classification for indexing during fingerprint matching. Zebbiche and Khelifi \hyperref[b10]{[11]} presented biometric images as one Region of Interest (ROI). The scheme consists of embedding the watermark into ROI in fingerprint images. Discrete Wavelet Transform and Discrete Fourier Transform are used for the proposed algorithm. Yi Chen and Anil K Jain \hyperref[b11]{[12]} proposed an algorithm based on fingerprint features viz., minutiae and ridges, Pattern and Pores. The correlation among Fingerprint features and their distributions are considered for the model.\par
Tachaphetpiboont and Amornraksa \hyperref[b12]{[13]} proposes a feature extraction method based on FFT for the fingerprint matching. The recognition rate obtained from the proposed method is also evaluated by the k-NN classifier. The amount of time required for the extraction and verification is very less in this approach. The matching stage is the process to compare the acquired feature with the template in the database ..In other words the process of matching stage is to calculate the degree of similarity between the input test image(for user when he wants to prove his/her identity)and a training image from database (the template which created at the time of enrolment).Matching can be done in three methods: hierarchical approach which employs simple but computationally effective features to retrieve a subset of templates in a given database. This approach increases matching speed at the cost of accuracy \hyperref[b13]{[14]}, classification: Classification approaches assign a class to each biometric in a database. There are many classification methods including KNN classifier \hyperref[b14]{[15]}.and Coding approaches will use one matching function to search entire databases. Arun Ross et. al., \hyperref[b15]{[16]} proposed the hybrid fingerprint matcher which employs the combination of ridge strengths and a set of minutiae points. Johg Ku Kum et. al., \hyperref[b16]{[17]} presented a study on Hybrid fingerprint matching methods. The minutiae and image based fingerprints verification methods are implemented together. The shapes in the fingerprint such as square, diamond, cross and dispersed cross are used for matching. Swapnali Mahadik et. al., \hyperref[b17]{[18]} described an Alignment based Minutiae Matching algorithm. The minutiae extraction involves Filtering, Binarization, Orientation Estimation, Region of interest, Thinning and Minutiae Extraction. In the matching stage the images are subjected to translation Rotation and Scaling. Anil Jain et. al., \hyperref[b18]{[19]} described the use of logistic regression method to integrate multiple fingerprint matching algorithms. The integration of Hough transform based matching, string distance based matching and 2D dynamic programming based matching using the logistic regression has minimized the False Rejection Rate for a specified level of False Acceptance Ratio. Aparecido Nilcau Marana and Jain \hyperref[b19]{[20]} proposed Ridge Based Fingerprint matching using the Hough transform. The major straight lines that match the fingerprint ridges are used to estimate rotation and translation parameters. 
\section[{Global Journal of Computer Science and Technology}]{Global Journal of Computer Science and Technology}\par
Volume XVI Issue II Version I  1996 Hough transform-based approaches ----------- \hyperref[b76]{[77]} 1997 Ridge-based relative pre-alignment ----------- \hyperref[b65]{[67]} 2004 Minutiae matching THU \hyperref[b77]{[78]} 2005 Global matching of clusters of minutiae ----------- \hyperref[b66]{[68]} 2006 Invariant moment finger Code and LVQ FVC2002 \hyperref[b79]{[80]} 2006 Global minutiae matching with image correlation ---------- \hyperref[b67]{[69]} 2007 Minutiae matching, vector matching ,weight modification and local area matching process FVC2002 \hyperref[b69]{[70]} 2008\par
Minutiae matching, which find the similartiy between two images and by calculating the correlation between these images.\par
-------- \hyperref[b82]{[83]} 2009 Global matching by evolutionary algorithms -------- \hyperref[b81]{[82]} 2010 Weighted global matching with adjustment of scores -------- \hyperref[b80]{[81]} 2012 Orientation image-based relative pre-alignment -------- \hyperref[b70]{[71]} 2013 LDP and SLFNN FVC2002 \hyperref[b78]{[79]} 2013 Hierarchical and/or multilevel minutiae matching -------- \hyperref[b72]{[73]} 2007 Minutiae matching, RMI and Fuzzy operator -------- \hyperref[b73]{[74]} 2012 ELM and R-ELM FVC2002 
\section[{IV. Palm Print}]{IV. Palm Print}\par
The palm used in fortune telling 3000 years ago, but in 1998 Wei and David \hyperref[b20]{[21]} studied the palm print as personal identification and it became one type of physical biometrics. Wei and David found that the features of palm print are geometry, principle lines (life, heart and head), wrinkle, delta point and minutiae. No two humans' palms are identical. The space of palm is greater than the fingerprint space so the palm had more information than a fingerprint. The palmprint is to contain principal lines and wrinkles in addition to pattern of ridges and valleys similar to fingerprints. The principle lines and wrinkles can be captured by a lower resolution sensor fig. \hyperref[fig_4]{4 (b)},whereas the ridges and valleys in palm are captured by high resolution . The ridges are shown as dark lines; and the valleys are the white lines between those black lines. The minutiae are the points where the ridges changed such as bifurcation and endpoint. The area of palm print is larger than the fingerprint area, then the number of minutes in a palm print around ten times the minutes in a fingerprint \hyperref[b21]{[22]}. The palm can be captured from normal scanners.  There are four types of devices that can capture the palm: CCD-based palmprint scanner, digital camera, digital scanner and video camera. The offline palmprint identification obtaines images by ink the area of palm and then put a sheet of white paper on the palm and then scans the paper to get a digital image \hyperref[b22]{[23]}. Zhang et al \hyperref[b23]{[24]} were the first research team to develop online palmprint identification (CCD-based palmprint scanner) and it captured high quality palmprint image. The CCD-based palmprint scanner is depended on the lens, camera and the light sources fig.  {\ref 6}.  \hyperref[b23]{[24]} presented the Gaussian smoothing for the original image of palmprint, then transformed it into binary image. After that it used the boundary tracing algorithm for detect the edges, then computed the tangent between the two gaps of fingers to get the Y-axis and finally extracted a sub image of a fixed size based on coordinate system. However, in \hyperref[b24]{[25]} it cropped the area of fingers to reduce the time of compute the tangent, and enhance the ROI to extend the gray scope into 256 to make the lines clear for feature extraction.\par
C. C. Han et al \hyperref[b25]{[26]} applied to full palmprint images (scanner image) , it used the border tracing algorithm after convert the image into binary image, then located the five fingers tips and four fingers roots by used wavelet based segmentation, and from the ring fingers points are establish the coordinate of ROI.\par
K. Chuang et al. \hyperref[b26]{[27]} applied the opening morphology operation for removing the noise of binary image of palm print, and then shrink the region of palm print image by segmented a rectangular region bounded by four lines: upper and lower bound should less than 200 white pixels, right and left bound should be less than 95 white pixels. It detected the boundary by using Sobel edge detection. Then, it took a double derivation of palm boundary to locate three points between the fingers. Next, it created a line by connecting the two points in the upper curve and lower curve, and this line used to align the difference palm print image. It created a point in the middle of the align line M. This point with the middle curve point used to establish the central point of coordinate of ROI.\par
In case of offline palm print image, no need for binarizing the palm print image because it is already black and white. R. Wang et. al. \hyperref[b27]{[28]} utilized Gaussian filter to remove the noise from the palm print image, and then used canny edge detection and convex hull to detected the end points of heart line and life line (datum points). 
\section[{Global Journal of Computer Science and Technology}]{Global Journal of Computer Science and Technology}\par
Volume XVI Issue II Version I  The feature extraction applied on the output of pre-processing phase which is a fixed size of image. And extract the feature of palm like principle lines, wrinkles and minutiae, and each feature belongs to a different resolution.\par
Wei and Zhang \hyperref[b28]{[29]} extracted the datum points and the line features from the palm print image. The datum points are defined as the points of palm print registration. Therefore, it detected the principle lines and their endpoints by using the directional projection algorithm. Moreover, the authors have improved template algorithm to extract the ridges and wrinkles as straight lines.\par
D. Zhang et al. \hyperref[b23]{[24]} since the stack filter algorithm is able to extract the principle lines of palm print, but the principle lines are not sufficient to prove the uniqueness of palm print. Thus, the author's proposed the 2D Gabor to represent the palm print for extracting the texture features of palm print from lowresolution.\par
J. Gan and D. Zhou \hyperref[b24]{[25]} decomposed the palm print image into sub-images by using the 2-dimensional multi-scale wavelet, then four images are obtained; one of those sub-images is the approximation image for lowfrequency components, and the rest of sub-images are demonstrated for the high-frequency component. After that, segment each wavelet sub-image into ?? 2 blocks C. C. Han et al \hyperref[b25]{[26]} applied four directions of Sobel operators to extract the feature points of ROI of palm print, and then applied a complex morphology operator to extract the features of palm print image.\par
Yao et al. \hyperref[b29]{[30]} proposed Gabor transformation to extract the texture of palm print features which divided the palm print image into 32 regions. And it was used eight direction (0, ??/8, ??/4,3??/8, ??/2,5??/8,3??/4,7??/8) and four scales \hyperref[b1]{(2,}\hyperref[b3]{4,}\hyperref[b7]{8,}\hyperref[b15]{16)} 8*4=32 regions to obtain the image texture characteristics. Then it was resized the domination of Gabor image into 1/16 of original image. After that, researchers used ICA (Independent Compo nent Analysis) for further extracted features.\par
The matching stage is to compare the acquired feature with the template in the database. In \hyperref[b28]{[29]} proposed the Euclidean distances to match between the endpoints of two lines. And computed the three parameters (slope, intercept and angle) of each line segmented in the two palm print images and decided whether the two lines are equal or not. But in \hyperref[b30]{[31]} it utilized the energy difference and Hausdroff distance to match between the two palms features. Gan and Zhou \hyperref[b24]{[25]} the matching based on Euclidean distance between feature vectors and NND (Nearest Neighbour Distance) rule.\par
D. Zhang et al. \hyperref[b23]{[24]} determined the similarity measurement of two palm print by using the Humming distance. And in \hyperref[b25]{[26]} authors proposed two verification mechanisms, one is the correlation function to measure the similarity between the two feature vectors, and the second is Back propagation neural network (BPNN) with the scaled conjugate-gradient algorithm. Also, researchers in \hyperref[b29]{[30]} identified the weight features by BBNN. X.Y Jing and D. Zhang \hyperref[b31]{[32]} took the first five samples of each individual in database as training samples and the reminders as test samples, and then the number of training and testing will be 950 training and 2090 testing. The first twenty low frequency bands are selected. Thus, the principle components are 210 and it obtained 181 discrimination vectors. In this paper the result of the recognition accuracy is 98.13\%. Year 2016 ( ) F d) Matching 
\section[{It contains pattern of ridges and Valleys}]{It contains pattern of ridges and Valleys}\par
It contains pattern of ridges and Valleys also it contains additional features such as principal lines, wrinkles, dathm points. 
\section[{2.}]{2.}\par
It is difficult to be captured even with the lower resolution scanner.\par
It is easy to be captured even with a lower resolution scanner. 
\section[{3.}]{3.}\par
Both deal with the some problems like noisy data, Non-universality, intra-class variations, spoof attack.etc. 
\section[{4.}]{4.}\par
The area of finger is less. The area of palm is much large in comparison to finger. 
\section[{5.}]{5.}\par
It is less distinctive It is more distinctive.\par
V. 
\section[{Multi-Modal}]{Multi-Modal}\par
The multimodal biometrics combine more than one modalities of biometrics to improve the recognition accuracy \hyperref[b36]{[37]}. The recognition system which acquires biometric information from many sources for the same person in order to determine the identity of a person known as multi-biometrics system. Any piece of evidence can be independently used to recognize a person is called a source of biometric information \hyperref[b37]{[38]}. Biometric systems are becoming popular as measures to identify human being by measuring one's physiological or behavioral characteristics. The multimodal biometric systems provide advantage over the conventional Unimodal biometric systems in various ways \hyperref[b38]{[39]}.\par
The main goals of multi-modal biometrics are to reduce at least one of the following; FAR (False Accept Rate), FRR (False Reject Rate), FTE (Failure To Enrollment rate) and Susceptibility to artifacts or mimics. But it also increases sensor cost, enrollment time, transit time and system development \hyperref[b36]{[37,}\hyperref[b38]{39]}.Multimodal biometric system acquires the input from one or more sensors measuring two or more different modalities of biometric characteristics. 
\section[{VI.}]{VI.}\par
[30] Proposed two steps for fusion the palm print and face feature at the feature level: firstly, since the huge difference between the face and palm then it normalized their features as certain range. Secondly, utilized User-specific weighting rule, where the weights of palm print are varies from 0.1 to 0.9, and the weights of face are varies from 0.9 to 0.1. Then selected the weight based on the highest recognition rate of all pairs weights of palm print and face varies weight.\par
In \hyperref[b32]{[33]} proposed fusion of face and palm print at the four levels and each level had difference techniques: at the sensor level used wavelets based image fusion scheme, at the feature level used few normalization techniques, at the score level used a some rules of fusion such as sum, max and min rule to combine the matching score, finally at the score level used a logical AND \& OR operators. raw biometric data (e.g., a face image) acquired from an individual represents the richest source of information although it is expected to be contaminated by noise (e.g., non-uniform illumination, background clutter, etc.). Sensor level fusion refers to the consolidation of (a) raw data obtained using multiple sensors, or (b) multiple snapshots of a biometric using a single sensor.\par
ii. Feature-level fusion In feature-level fusion, the feature sets originating from multiple biometric algorithms are consolidated into a single feature set by the application of appropriate feature normalization, transformation and reduction schemes. The primary benefit of feature-level fusion is the detection of correlated feature values generated by different biometric algorithms and, in the process, identifying a salient set of features that can improve recognition accuracy. Eliciting this feature set typically requires the use of dimensionality reduction methods and, therefore, feature-level fusion assumes the availability of a large number of training data. Also, the feature sets being fused are typically expected to reside in commensurate vector space in order to permit the application of a suitable matching technique upon consolidating the feature sets.\par
iii. Score-level fusion In score-level fusion the match scores output by multiple biometric matchers are combined to generate a new match score (a scalar) that can be subsequently used by the verification or identification modules for rendering an identity decision. Fusion at this level is the most commonly discussed approach in the biometric literature primarily due to the ease of accessing and processing match scores (compared to the raw biometric data or the feature set extracted from the data). Fusion methods at this level can be broadly classified into three categories: density-based schemes \hyperref[b54]{[56]}, transformation-based schemes \hyperref[b56]{[58]} and classifier based schemes. The fig 8  {\ref .}show levels of fusions. 
\section[{iv. Decision-level fusion}]{iv. Decision-level fusion}\par
Many commercial off-the-shelf (COTS) biometric matchers provide access only to the final recognition decision. When such COTS matchers are used to build a multi biometric system, only decision level fusion is feasible. Methods proposed in the literature for decision level fusion include "AND" and "OR" rules \hyperref[b55]{[57]}, majority voting weighted majority voting, Bayesian decision fusion the Dumpster-Shafer theory of evidence and behavior knowledge space \hyperref[b57]{[59]}.   BBNN for recognition the fusion \hyperref[b32]{[33]} Face \& palmprint All levels ----------------- \hyperref[b34]{[35]} Face \& palmprint Feature level ------------------- \hyperref[b35]{[36]} Palmprint \& fingerprint Feature level Fuzzy vault -------- Fingerprint and voice Match score Functional link network \hyperref[b51]{[53]} Many researches for person verification using multi biometrics with decision fusion traits are done.\par
Table10 : Summarized most important researches \hyperref[b53]{[55]} Researcher /Year Multibiometric traits Algorithm Arun R., et al /2004 
\section[{Information fusion in biometrics}]{Information fusion in biometrics}\par
The research used score level fusion multibiometrics system by combining three traits(face, fingerprint and hand geometry) are presented, using compare for the feature extraction in each single traits \hyperref[b4]{[5]} Rajiv The researchers applied palmprint and hand geometry over other biometric modalities. It implemented particle swarm based optimization technique for selecting optimal parameters through decision level fusion of two modalities: palmprint and hand geometry \hyperref[b40]{[42]}. 
\section[{Global Journal of Computer Science and Technology}]{Global Journal of Computer Science and Technology}\par
Volume XVI Issue II Version I This research applied likelihood ratio-based score fusion and Bayesian approach for consolidating ranks and a hybrid scheme that utilizes both ranks and scores to perform fusion in identification systems \hyperref[b41]{[43]}. 
\section[{Giot R., et al /2010}]{Giot R., et al /2010}\par
Fast Learning For Multibiometrics Systems Using Genetic Algorithms This research use algorithm to learn the parameters of different multibiometrics fusion functions. It interested in biometric systems usable on any computer (they do not require specific material). In order to improve the speed of the learning, we defined a fitness function based on a fast ERR, FAR and GAR also, the search calculate the time that required to recognition the person \hyperref[b11]{[12]} It presents a feature level fusion algorithm based on texture features. The system combines fingerprint, face and off-line signature. Texture features are extracted from Curvelet transform. The Curvelet feature dimension is selected based on d-prime number \hyperref[b43]{[45]}. 
\section[{VII. Conclusions}]{VII. Conclusions}\par
This paper gave an overview of the fingerprint and palm print recognition . We highlighted in details the fingerprint and palm separately. We also referred to the image acquisition stage , image pre-processing stage, feature extraction stage and matching stage for recognition purpose in details. In addition to that we introduced some techniques for both modalities .Also ,we gave an elaboration about multimodal biometric system recognition and the fusion of biometric trait.\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-4.png}
\caption{\label{fig_2}3}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Figure 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-6.png}
\caption{\label{fig_4}Figure 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5}\includegraphics[]{image-7.png}
\caption{\label{fig_5}Figure 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{67}\includegraphics[]{image-8.png}
\caption{\label{fig_6}Figure 6 :Figure 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-9.png}
\caption{\label{fig_7}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{8}\includegraphics[]{image-10.png}
\caption{\label{fig_8}Figure 8 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{9}\includegraphics[]{image-11.png}
\caption{\label{fig_9}Figure 9 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.0775\textwidth}P{0.045\textwidth}P{0.6625\textwidth}P{0.065\textwidth}}
Ref\tabcellsep Year\tabcellsep Pre-processing\tabcellsep Database\\
{}[67]\tabcellsep 2004\tabcellsep Orintation field :Modal-based method, region segmention, orientation filed, ridge enhancement\tabcellsep THU\\
{}[68]\tabcellsep 2006\tabcellsep Hierarchical Discrete wavelet Transformation(DWT)\tabcellsep FVC2002\\
{}[69]\tabcellsep 2007\tabcellsep Gabor filters, mask estimation, Binarization, Thinnig\tabcellsep FVC2002\\
{}[70]\tabcellsep 2008\tabcellsep Minutiae feature by using CNN\tabcellsep \\
{}[71]\tabcellsep 2013\tabcellsep Normalization, Ridge segmention, Ridge orintation Core point detection.\tabcellsep FVC2002\\
{}[74]\tabcellsep 2012\tabcellsep Enhancement using two stage determination of reference point and determination of ROI\tabcellsep FVC2002\\
{}[73]\tabcellsep 2007\tabcellsep Gray scale image, binarization\tabcellsep ------\\
{}[84]\tabcellsep 2013\tabcellsep Gabor filter and FFT, Normalization, local orientation, local frequency, region mask, filter, Binarization\tabcellsep FVC2004\\
\multicolumn{3}{l}{c) Feature extraction stage}\tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.09256308100929615\textwidth}P{0.07224435590969455\textwidth}P{0.5756972111553784\textwidth}P{0.1094953519256308\textwidth}}
Ref\tabcellsep Year\tabcellsep Feature extraction\tabcellsep Database\\
{}[60]\tabcellsep 1992\tabcellsep Orientation field\tabcellsep NIST4\\
{}[61]\tabcellsep 1996\tabcellsep Singularities\tabcellsep NIST4\\
{}[62]\tabcellsep 1998\tabcellsep Ridge structure\tabcellsep NIST4\\
{}[63]\tabcellsep 1999\tabcellsep Singularities and ridge\tabcellsep NIST4\\
{}[64]\tabcellsep 2001\tabcellsep Fingercode\tabcellsep NIST4\\
{}[65]\tabcellsep 2002\tabcellsep Ridge Distribution\tabcellsep NIST4\\
{}[66]\tabcellsep 2003\tabcellsep Relational graph, fingercode\tabcellsep NIST4\\
{}[67]\tabcellsep 2004\tabcellsep Minutiae extraction\tabcellsep THU\\
{}[68]\tabcellsep 2006\tabcellsep Seven Invariant moment, fingercode, refrences point\tabcellsep FVC2002\\
{}[69]\tabcellsep 2007\tabcellsep Ridge ending and ridge bifurcation\tabcellsep FVC2002\\
{}[70]\tabcellsep 2008\tabcellsep Minutiae feature by using CNN\tabcellsep \\
A dina\tabcellsep 2012\tabcellsep Scale Invariant Feature Transformtion (SIFT)\tabcellsep FVC2002\\
{}[71]\tabcellsep 2013\tabcellsep ROI,Compute LDP Code (local Directional pattern)\tabcellsep FVC2002\\
{}[72]\tabcellsep 2014\tabcellsep Fixed length represntion that provide extract aligment between features.\tabcellsep FVC2002/ FVC2004\\
{}[74]\tabcellsep 2012\tabcellsep Local and globle Invariant moment Feature and PCA for feature selection\tabcellsep FVC2002\\
\multicolumn{2}{l}{d) Matching stage}\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_1}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.22037037037037036\textwidth}P{0.1259259259259259\textwidth}P{0.2518518518518518\textwidth}P{0.2518518518518518\textwidth}}
Ref\tabcellsep Year\tabcellsep Matching\tabcellsep Database\\
{}[76]\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.17061482820976492\textwidth}P{0.20443037974683545\textwidth}P{0.16139240506329114\textwidth}P{0.31356238698010847\textwidth}}
Author\tabcellsep Remove noise\tabcellsep Edge detection\tabcellsep Key points\\
name\tabcellsep \tabcellsep \tabcellsep \\
D. Zhang et al. [24]\tabcellsep Gaussian smoothing then Binarizing\tabcellsep Boundary tracking algorithm\tabcellsep Gap fingers tangent\\
K. Chuang et al. [27]\tabcellsep Binarizing then opening operation\tabcellsep Sobel edge detection\tabcellsep Double derivation and get 3 points between fingers\\
C. C. Han et al [26]\tabcellsep Binarizing by using threshold histogram\tabcellsep Border tracing algorithm\tabcellsep Wavelet to locate the five fingers tips and four fingers root\\
R. Wang et. al.[28]\tabcellsep Gaussian filter\tabcellsep Canny edge detection\tabcellsep Convex hull to detect the end points of heart line and life line\\
\multicolumn{2}{l}{c) Feature Extraction}\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_5}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.046158940397350995\textwidth}P{0.17562913907284766\textwidth}P{0.23529801324503308\textwidth}P{0.005629139072847682\textwidth}P{0.2139072847682119\textwidth}P{0.1733774834437086\textwidth}}
Ref\tabcellsep Feature based\tabcellsep \multicolumn{2}{l}{Feature extraction}\tabcellsep Matching technique\tabcellsep Database\\
no\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
{}[29]\tabcellsep Straight lines\tabcellsep \multicolumn{2}{l}{Directional projection}\tabcellsep Euclidian distance\tabcellsep Offline, 200 samples\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{algorithm}\tabcellsep \tabcellsep \\
{}[31]\tabcellsep Texture \& feature points\tabcellsep --------\tabcellsep \tabcellsep Hausdroff distance Energy different \&\tabcellsep Offline, 200 samples\\
{}[24]\tabcellsep Lines \& textures\tabcellsep Stack filter\tabcellsep \& 2D\tabcellsep Humming distance\tabcellsep Online, 193*40 samples\\
\tabcellsep \tabcellsep Gabor\tabcellsep \tabcellsep \tabcellsep \\
{}[33]\tabcellsep Textures\tabcellsep LPQ\tabcellsep \tabcellsep --------\tabcellsep PolyU 189*20\\
{}[26]\tabcellsep Lines feature\tabcellsep \multicolumn{2}{l}{Sobel operator \& morphology}\tabcellsep Correlation function \& BPNN\tabcellsep --------\\
{}[25]\tabcellsep Features vector\tabcellsep \multicolumn{2}{l}{Multi-scale wavelet}\tabcellsep Euclidean distance \&\tabcellsep Online, 100*60 samples\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep NND rules\tabcellsep \\
{}[30]\tabcellsep Texture\tabcellsep \multicolumn{2}{l}{Gabor transformation \&}\tabcellsep BPNN\tabcellsep 50*10 samples\\
\tabcellsep \tabcellsep ICA\tabcellsep \tabcellsep \tabcellsep \\
{}[34]\tabcellsep Orientation features\tabcellsep \multicolumn{2}{l}{Six Gabor filter on diff direction}\tabcellsep Humming distance\tabcellsep --------\\
{}[32]\tabcellsep Discriminant DCT\tabcellsep \multicolumn{2}{l}{Improve Fisher Palm}\tabcellsep Neural network\tabcellsep Online 190*16 samples\\
\tabcellsep features\tabcellsep method\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_6}Table 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7} \par 
\begin{longtable}{P{0.136\textwidth}P{0.374\textwidth}P{0.34\textwidth}}
No\tabcellsep Fingerprint\tabcellsep Palm print\\
1.\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_7}Table 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{8} \par 
\begin{longtable}{P{0.059500000000000004\textwidth}P{0.323\textwidth}P{0.306\textwidth}P{0.11900000000000001\textwidth}P{0.0425\textwidth}}
Ref\tabcellsep Biometrics modalities\tabcellsep \multicolumn{2}{l}{Fusion level Techniques}\tabcellsep Notes\\
{}[30]\tabcellsep Face \& palmprint\tabcellsep feature level\tabcellsep Weighting\\
\tabcellsep \tabcellsep \tabcellsep rules\end{longtable} \par
 
\caption{\label{tab_8}Table 8 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{9} \par 
\begin{longtable}{P{0.3301652892561983\textwidth}P{0.16859504132231404\textwidth}P{0.29416322314049587\textwidth}P{0.05707644628099173\textwidth}}
Modality\tabcellsep Level of Fusion\tabcellsep Fusion Strategies\tabcellsep Authors\\
Palmprint and Face\tabcellsep Matching Level\tabcellsep Sum of Score\tabcellsep {}[40]\\
Fingerprint and Face\tabcellsep Score and Decision\tabcellsep Sum Rule and Likelihoods\tabcellsep {}[41]\\
Face, Fingerprint, and\tabcellsep Matching Level\tabcellsep Sum Rule\tabcellsep {}[42]\\
Hand Geometry\tabcellsep \tabcellsep \tabcellsep \\
Fingerprint and Hand-\tabcellsep Combination Approach\tabcellsep Sum, Max, Min Scores\tabcellsep {}[43]\\
Geometry\tabcellsep \tabcellsep \tabcellsep \\
Fingerprint, Palmprint,\tabcellsep Feature Level\tabcellsep ANN\tabcellsep {}[44]\\
and Hand-Geometry\tabcellsep \tabcellsep \tabcellsep \\
Face and Fingerprint\tabcellsep Matching Level\tabcellsep Sum, Min-Max, and Zscore\tabcellsep {}[44]\\
Face and palmprint\tabcellsep Feature\tabcellsep Feature concatenation\tabcellsep {}[ 45]\\
Fingerprint and signature\tabcellsep Match score\tabcellsep SVM in which quality measures\tabcellsep {}[46]\\
\tabcellsep \tabcellsep are incorporated\tabcellsep \\
Face and fingerprint\tabcellsep Match Score\tabcellsep Product rule\tabcellsep {}[47]\\
Face, fingerprint and\tabcellsep Match Score\tabcellsep Likelihood ratio\tabcellsep {}[48]\\
voice\tabcellsep \tabcellsep \tabcellsep \\
Face, fingerprint and\tabcellsep Match Score\tabcellsep Sum rule; decision trees; linear\tabcellsep {}[ 49]\\
hand geometry\tabcellsep \tabcellsep discriminant function\tabcellsep \\
Face and fingerprint\tabcellsep Match Score\tabcellsep Sum rule, Weighted sum rule\tabcellsep {}[50]\\
Fingerprint, hand\tabcellsep Match score\tabcellsep Weighted sum rule\tabcellsep {}[51]\\
geometry and voice\tabcellsep \tabcellsep \tabcellsep \\
Fingerprint and\tabcellsep Match score\tabcellsep Reduced multivariate\tabcellsep {}[52]\\
hand geometry\tabcellsep \tabcellsep polynomial model\tabcellsep \end{longtable} \par
 
\caption{\label{tab_9}Table 9 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.18478260869565216\textwidth}P{0.5490683229813664\textwidth}P{0.11614906832298137\textwidth}}
.J, et\tabcellsep \multicolumn{2}{l}{Multimodal}\\
al /2006\tabcellsep Biometric\tabcellsep using\\
\tabcellsep \multicolumn{2}{l}{Face, Iris, palmprint}\\
\tabcellsep and\tabcellsep Signature\\
\tabcellsep Features\tabcellsep \\
Kumar, A,\tabcellsep \multicolumn{2}{l}{Fusion of Hand}\\
et al. /2008\tabcellsep \multicolumn{2}{l}{Based Biometrics}\\
\tabcellsep using\tabcellsep Particle\\
\tabcellsep \multicolumn{2}{l}{Swarm optimization}\end{longtable} \par
 
\caption{\label{tab_10}}\end{figure}
 			\footnote{© 2016 Global Journals Inc. (US)} 		 		\backmatter  			  				\begin{bibitemlist}{1}
\bibitem[Wangyuan]{b67}\label{b67} 	 		\textit{},  		 			Wangyuan 		.  		 	 
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\end{document}
