Multimodal Biometrics Recognition Using a Deep Convolutional Neural Network with Transfer Learning in Surveillance Videos
| dc.contributor.author | Aung, Hsu Mon Lei | |
| dc.contributor.author | Pluempitiwiriyawej, Charnchai | |
| dc.contributor.author | Hamamoto, Kazuhiko | |
| dc.contributor.author | Wangsiripitak, Somkiat | |
| dc.date.accessioned | 2026-08-06T10:37:15Z | |
| dc.date.available | 2026-08-06T10:37:15Z | |
| dc.date.issued | 2022-07-01 | |
| dc.description.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. | |
| dc.identifier.citation | Computation, 10(7), 2022 | |
| dc.identifier.doi | 10.3390/computation10070127 | |
| dc.identifier.issn | 20793197 | |
| dc.identifier.other | 2-s2.0-85148351065 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/13290 | |
| dc.source | Computation | |
| dc.subject | deep CNNs | |
| dc.subject | human recognition | |
| dc.subject | multimodal biometrics | |
| dc.subject | transfer learning | |
| dc.title | Multimodal Biometrics Recognition Using a Deep Convolutional Neural Network with Transfer Learning in Surveillance Videos | |
| dc.type | Article |