After 9/11 tragedy, governments in all over the world started to look more seriously to the levels of security they have at their airports and borders. Countries annual budgets were increased drastically to have the most recent technologies in identification, recognition and tracking of suspects. The demand growth on these applications helped researchers to be able to fund their research projects. One of most common biometric recognition techniques is face recognition. Although face recognition is not as accurate as the other recognition methods using iris or fingerprints, it still grabs huge attention of many researchers in the field of computer vision. The main reason behind this attention is the fact that the face is the conventional and the fastest way people use to identify each other. Since the mid of last century the researchers have been working extensively in the field of face recognition trying to introduce new approaches or improve the performance of the available approaches to obtain robust and accurate face recognition systems.

Face recognition is emerging as an active research area spanning many research branches such as image processing, pattern recognition, computer vision, and neural networks. Faces are one of the biometrics that humans often use in order to recognize individuals. Researchers have shown significant improvements in computing capability over the few decades, and nowadays enable to simulate similar recognitions automatically. In addition, face recognition technology has many commercial applications that range from static matching of controlled format photographs such as photos on identification cards to real time matching of video sequences. Although humans are remembering the faces quickly than other objects and seem to recognize faces easily, machine recognition is a complicated task. A general issue in this field is as follows: For a given image or video sequence, identify one or more person in the scene using a training database of faces. The solution of this issue involves segmentation of faces from non-face background objects, extraction of meaningful features from the face region, identification, and matching.

In this book, various state-of-art techniques about face recognition has been studied. The main portion of the book discuss about the face recognition system based on the probability distribution functions (PDF) of pixels in colour channels. This book studies into two main issues of face recognition, one of which is facial image illumination enhancement and the other one is classification stage where the face images are being recognized. In the pre-processing stage a novel facial image equalization method is studied and discussed. Further more PDF based face recognition is analytically is studied.

In this book, a new high performance face recognition system based on matching the colour pixel statistics is introduced. A pre-processing phase is introduced and applied to segment faces from the background. Furthermore, a dedicated image equalization method is introduced and implemented to minimize the illumination problems of the images for further processing. The histogram of the segmented face image as pixel statistics feature is used for face recognition by cross correlating the histogram of a given face and the histograms of faces in the database. Alternatively the probability distribution functions of the images in different colour channels, together with the Kullback-Leibler Divergence/Distance (KLD) metric is also used for the recognition of faces. Majority voting (MV) and feature vector fusion (FVF) methods is briefly introduced and applied to combine feature vectors obtained from different colour channels in HSI and YCbCr colour spaces to improve recognition performance.