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

1,2,†, 1,2,†, 1,2,* and 1,2
School of Electronic and Information Engineering, Shouthwest University, Chongqing 400715, China
Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing, Southwest University, Chongqing 400715, China
Author to whom correspondence should be addressed.
These authors contribute equally to this work.
Academic Editor: Willy Susilo
Information 2016, 7(4), 61;
Received: 16 July 2016 / Revised: 22 October 2016 / Accepted: 24 October 2016 / Published: 27 October 2016
PDF [2301 KB, uploaded 27 October 2016]


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. View Full-Text
Keywords: near-infrared face recognition; illumination invariance; convolutional neural network near-infrared face recognition; illumination invariance; convolutional neural network

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Peng, M.; Wang, C.; Chen, T.; Liu, G. NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification. Information 2016, 7, 61.

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