Abstract
Biometric recognition is a critical task in security control systems. Although the face has long been widely accepted as a practical biometric for human recognition, it can be easily stolen and imitated. Moreover, in video surveillance, it is a challenge to obtain reliable facial information from an image taken at a long distance with a low-resolution camera. Gait, on the other hand, has been recently used for human recognition because gait is not easy to replicate, and reliable information can be obtained from a low-resolution camera at a long distance. However, the gait biometric alone still has constraints due to its intrinsic factors. In this paper, we propose a multimodal biometrics system by combining information from both the face and gait. Our proposed system uses a deep convolutional neural network with transfer learning. Our proposed network model learns discriminative spatiotemporal features from gait and facial features from face images. The two extracted features are fused into a common feature space at the feature level. This study conducted experiments on the publicly available CASIA-B gait and Extended Yale-B databases and a dataset of walking videos of 25 users. The proposed model achieves a 97.3 percent classification accuracy with an F1 score of 0.97and an equal error rate (EER) of 0.004.
Subject
Applied Mathematics,Modeling and Simulation,General Computer Science,Theoretical Computer Science
Reference40 articles.
1. Handbook of Fingerprint Recognition;Maltoni,2009
2. Iris recognition based on pca for person identification;Buddharpawar;Int. J. Comput. Appl.,2015
3. Finger Vein Recognition Using Local Line Binary Pattern
4. Person recognition based on fusion of iris and periocular biometrics;Joshi;Proceedings of the 2012 12th International Conference on Hybrid Intelligent Systems (HIS),2012
5. Individual recognition using gait energy image
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