NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

作者：Peng Min; Wang Chongyang; Chen Tong; Liu Guangyuan

来源：Information, 2016, 7(4): UNSP 61.

DOI：10.3390/info7040061

• Chinese Academy of Sciences
• Compact structures
• Cooperative users
• Face identification
• Face recognition methods
• Identification rates
• Illumination invariance
• Near Infrared

Near-infrared (NIR) face recognition has attracted increasing attention because of its advantage of illumination invariance. However, traditional face recognition methods based on NIR are designed for and tested in cooperative-user applications. In this paper, we present a convolutional neural network (CNN) for NIR face recognition (specifically face identification) in non-cooperative-user applications. The proposed NIRFaceNet is modified from GoogLeNet, but has a more compact structure designed specifically for the Chinese Academy of Sciences Institute of Automation (CASIA) NIR database and can achieve higher identification rates with less training time and less processing time. The experimental results demonstrate that NIRFaceNet has an overall advantage compared to other methods in the NIR face recognition domain when image blur and noise are present. The performance suggests that the proposed NIRFaceNet method may be more suitable for non-cooperative-user applications.

• 出版日期2016-12
• 单位西南大学