Research Area:  Machine Learning

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.

Keywords:  
Multimodal
Biometrics Recognition
Deep Convolutional Neural Network
Transfer Learning
Surveillance Videos
Deep Learning
Machine Learning

Author(s) Name:  Hsu Mon Lei Aung ,Charnchai Pluempitiwiriyawej,Kazuhiko Hamamoto and Somkiat Wangsiripitak

Publisher name:  MDPI

DOI:  10.3390/computation10070127

Volume Information:  Volume 10, Issue 7

Paper Link:   https://www.mdpi.com/2079-3197/10/7/127