Normalization of Infrared Facial Images under Variant Ambient Temperatures

Face recognition, being straightforward, passive and non-invasive comparing with other biometrics such as fingerprint recognition (Yang et al., 2006; Yang & Park, 2008a; Yang & Park, 2008b), has a nature place in biometric technology and computer vision. Currently, most researches on face recognition focus on visual images. The reason is obvious: such sensors are cheap and visual images are widely used. The key problem in visual face recognition is to cope with different appearances due to the large variations both in intrinsic (pose, expression, hairstyle etc) and extrinsic conditions (illumination, imaging system etc). It is difficult to find the unique characteristics for each face, and it is accordingly not easy to develop a reliable system for face recognition by using visual images. Infrared face recognition, being light-independent and not vulnerable to facial skin, expressions and posture, can avoid or eliminate the drawbacks of face recognition in visible light. Some methods (Buddharaju, et al, 2004, Chen, et al, 2005, Kong, et al, 2005, Wu, et al, 2005A) based on thermal images are proposed for infrared face recognition in last decade. It is highlighted that the infrared images which are the character of the human skins can be affected by ambient temperature, psychological, as well as physiological conditions. Therefore, the recognition systems based on thermal images have the problem that achieving high performance when the test and train images are captured in the same ambient temperature, while the performance is poor if the test and train samples are collected under different temperature (time-lapse data). To improve the performance on time-lapse data, it is important to normalize the training images and test images. Linear gray transform and histogram equalization are two common methods for image normalization. However, these approaches change the grayscales which represents the skin temperature so that the thermal images have no physical significance. Therefore, both methods are not suitable for normalization of infrared facial images. In this chapter, we dedicate to provide a novel study on normalization of infrared facial images, especially resulting from variant ambient temperatures. Three normalization methods are proposed to eliminate the effect of variant ambient temperatures. The experimental results show that the proposed methods can increase the robustness of infrared face recognition system and greatly improve its performance on time-lapse data. The organization of the chapter is as below. In section 2, effect of ambient temperatures on thermal images is analyzed. Three normalization methods are presented in Section 3. An