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Multimodal Biometrics Recognition Using a Deep Convolutional Neural Network with Transfer Learning in Surveillance Videos
| Content Provider | MDPI |
|---|---|
| Author | Aung, Hsu Mon Lei Pluempitiwiriyawej, Charnchai Hamamoto, Kazuhiko Wangsiripitak, Somkiat |
| Copyright Year | 2022 |
| Description | 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. |
| Starting Page | 127 |
| e-ISSN | 20793197 |
| DOI | 10.3390/computation10070127 |
| Journal | Computation |
| Issue Number | 7 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-07-21 |
| Access Restriction | Open |
| Subject Keyword | Computation Industrial Engineering Multimodal Biometrics Human Recognition Deep Cnns Transfer Learning |
| Content Type | Text |
| Resource Type | Article |
