Face and gait biometrics authentication system based on simplified deep neural networks

Research on biometric methods has gained momentum due to the increase in security concerns. Face and gait biometrics are two biometrics that are safe and non-invasive in nature and can be captured without the knowledge of the person. These two biometric can be easily used in surveillance applications. This paper presents a multimodal biometrics system based on face and gait based on principal component analysis (PCA) along with a simplified deep neural network (S-DNN). In the S-DNN analysis, cross entropy instead of Euclidean distance is considered. In the proposed method, PCA is used for feature extraction, and for the re-construction of faces, in place of inverse PCA, an artificial neural network is considered to improve the accuracy. The combination of PCA and ANN is separately used for both the biometrics and the matching score for each biometric identifier is obtained. Finally, the softmax function is used for the fusion of matching scores to get the final matching score. The main advantages of the proposed methodology are higher accuracy (99.51%) and very little processing time. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.