Abstract

Gait is a biometric trait that has been used for user authentication or verification on the basis of various attributes of gait. Gait of an individual get affected due to variation in mood, emotions, age and weight, due to these variation a perfect model is not possible that can be developed so that these all factors can be eliminated. In the proposed work, CASIA dataset has been used as standard dataset. This dataset contains samples of 16 different individuals that have been taken at 0, 45, 90 degrees of angles. Afterwards, silhouette images have been taken for feature extraction from the gait samples using variable2-dimenssiaonl principal component analysis with neural network classifier.Along with this, validation of the proposed work has been done using two performance evaluation parameters, namely, FAR and FRR through confusion matrix.