Face Recognition Based on Near-Infrared Light Using Mobile Phone

Authors:
Song-Yi Han;Hyun-Ae Park;Dal-Ho Cho;Kang Ryoung Park;Sangyoun Lee
Affiliations:
Dept. of Computer Science, Sangmyung University, Biometrics Engineering Research Center, 7 Hongji-Dong, Jongro-gu, Seoul, Republic of Korea;Image Sensor & Vision Technology TM Pixelplus Co., Ltd.,;Dept. of Computer Science, Sangmyung University, Biometrics Engineering Research Center, 7 Hongji-Dong, Jongro-gu, Seoul, Republic of Korea;Division of Digital Media Technology, Sangmyung University, Biometrics Engineering Research Center, 7 Hongji-Dong, Jongro-gu, Seoul, Republic of Korea;Biometrics Engineering Research Center, Dept. of Electrical and Electronic Engineering, Yonsei University, 134 Shinchon-dong, Seodaemon-ku, Seoul, Republic of Korea
Venue:
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Year:
2007

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Abstract

Recently, many companies have attempted to adopt biometric technology in their mobile phones. In this paper, we propose a new NIR (Near-Infra-Red) lighting face recognition method for mobile phones by using mega-pixel camera image. This paper presents four advantages and contributions over previous research. First, we propose a new eye detection method for face localization for mobile phones based on corneal specular reflections. To detect these SRs (Specular Reflections) (even for users with glasses), we propose successive On/Off activation of the dual NIR illuminators of mobile phone. Second, because the face image is captured by the NIR illuminator, the nose area can be highly saturated, which can degrade face recognition accuracy. To overcome this problem, we use a simple logarithmic image enhancement method, which is suitable for mobile phones with low processing power. Third, considering the low processing speed of mobile phones, we adopt integer-based PCA (Principal Component Analysis) method for face recognition excluding floating-point operation. Fouth, by comparing the recognition performance using the integer-based PCA to those using LDA (Linear Discriminant Analysis) and ICA (Independent Component Analysis) methods, we could know that the integer-based PCA showed better performance apt for mobile phone with NIR image.