Recent Advancements in Gabor Wavelet based Face Recognition Iqbal Nouyed 1 , M. Ashraful Amin 2 1 Computer Vision and Cybernetics Research Group, SECS, IUB 2 Assistant Professor, SECS, IUB Abstract Because existing face recognition systems lack required accuracy when viewpoint, illumination, expression, occlusion, accessories and so on vary considerably, continued research in this field of biometrics is a challenging objective. It is widely accepted that local features in face images are more robust against such distortions and a spatial-frequency analysis is often more desirable to extract such features. Having good characteristics of space-frequency localization, wavelet analysis is the right choice for this purpose. Among various wavelet bases Gabor functions provide optimized resolution in both spatial and frequency domains and it have been found to yield distortion tolerant feature spaces for pattern recognition tasks. This chapter discusses the recent advancements in the field of Gabor wavelet based face recognition. Introduction For facial feature representation Gabor wavelets have been successfully applied in various approaches. (Shen, 2005) classified the methods into three categories: analytic (feature based), holistic (global) and hybrid methods. Analytic approaches compare the salient facial features or components. Holistic approaches make use of the information derived from the whole face pattern. And, hybrid methods combine both local and global features to produce a more complete representation of human face. Excellent reviews on Gabor wavelet based face recognition have been done by (Shen, 2005) (Shen & Bai, 2006) and (Serrano, Diego, Conde, & Cabello, 2010). In this chapter we discuss the latest developments in Gabor wavelet based face recognition in last few years. Holistic Approaches Over the last few years, different approaches have been proposed to improve holistic methods for face recognition. Some of them include color processing, different face representations and image processing techniques to increase robustness against illumination changes. One of the most successful strategies has shown to be the use of Gabor representation of the images. There has been also some research about the combination of different recognition methods, both at the feature and score levels. (Tenllado, Gomez, Setoain, Mora, & Prieto, 2010) proposed an effective combination scheme that is able to improve a single holistic method by fusing the recognition scores obtained from both natural face images and their Gabor representations. These results suggest that some complementariness exists between both representations, which can be easily exploited by fusion at the score level. However, the face recognition problem is made difficult by the great variability in head rotation and tilt, lighting intensity and angle, facial expression, aging, partial occlusion (e.g. Wearing Hats, scarves, glasses etc.), etc. A multi-scale representation technique for face recognition using Gabor filter and Log-Gabor filter was proposed by (Murugan, Arumugam, Rajalakshmi, & Manish, 2010) to handle this issue. Gabor features have been known to be effective for face recognition. But, only a few approaches utilize phase feature and they usually perform worse than those using magnitude feature. For this reason, only the magnitudes of the Gabor coefficients are thought of as being useful for face