Face Recognition Technology Under Variant Illumination

Face recognition has attracted considerable interests in the recent years within the community of computer vision and pattern recognition. As one of the most successful branches of personal identification, it has great potential applications in public security, visual surveillance, digital personal identification, electronic commerce, multimedia, and digital entertainment, etc. The face recognition has developed rapidly over the past 40 years. Now under the controlled conditions, face recognition systems have achieved good results, and some versatile commercial recognition software have been appeared. However, face recognition technologies are currently far from mature. A great number of challenges are still leaved to resolve before one can implement a robust and practical face recognition application. Among these challenges, the illumination circumstance is one of the most difficult.Our work is focusing on the effect of illumination on the face recognition. The emphases of the work are the image preprocessing, feature point location, feature extraction and the classification under the varying illumination circumstance. The work and the innovation in this dissertation can be summarized as following.(1) Facial image preprocessing under varying illumination was studied.The illumination is one of the bottlenecks that affect recognition performance. In most cases, the difference between two images caused by illumination is greater than that caused by individual difference. Motivated by the illumination-reflectance model, a preprocessing algorithm based on the illumination-reference is proposed. Firstly, the algorithm constructs an illumination reference model utilizing a set of face images of different persons under normal illumination conditions. Following that, the testing face images are adjusted according to the illumination reference model. Lastly, the difference between Gaussian filters is used to smooth the boundary of the images that has been adjusted. Experimental results exhibits that the proposed algorithm could reduce the effect of the illumination. Meanwhile, the accuracy and robustness of the recognition system are improved.(2) Facial feature alignment under varying illumination was studied.Accurate facial feature alignment is the prerequisite of a face recognition system. Currently, the Active Shape Model (ASM) and Active Appearance Model (AAM) are the main models for this problem. However, two models mentioned above are sensitive to the illumination variation. To fight with the disadvantages of two models, an improved AAM under varying illumination is proposed. Firstly, the eyes are located by using the phase congruency binary edge image, which is used to initialize the model. Secondly, the model is constructed by utilizing features that are illumination robustness. After that, face is aligned coarsely. Finally, the facial images are segmented into some sub-regions, thus the improved AAM is used to get fine location in every sub-region. Encouragingly, experiment results have illustrated the better performance and illumination robustness of the proposed model.(3) Facial feature extraction under varying illumination was studied.Feature extraction is a key step to face recognition. The extracted feature should be robust to illumination, poses, expression, and age, etc. We generalize the conventional LPP algorithm to yield a new supervised algorithm. We name it as supervised LPP, which can deal with linear discriminant analysis in the features derived from NMF. In this way, a discriminant feature subspace having the maximum intersubject variation and the minimum intrasubject variation is established. Following that, the supervised LPP is extended to the two dimensions, thus a new two dimension supervised LPP is introduced. The results in numerical experiments show that the two proposed algorithms have higher recognition rates than the traditional subspace algorithms under varying illumination, which show they are illumination robustness.The Gabor wavelet has good characteristics of spatial location and orientation selection. Based on these observations, a Gabor subspace feature extraction algorithm is introduced, which attempts to fuse the Gabor phase and magnitude information. The proposed algorithm blend the merits of Gabor wavelet-based and subspace-based algorithm. In addition, it is illumination robustness.(4) Classification problem under varying illumination was studied.The classifier design is the last step of the face recognition. The Nearest Neighbor (NN) and Support Vector Machine (SVM) are traditional classifiers, which uses the Euclidean distances in the similarity measurement between different features. However, the Euclidean distances can't be used to measure the nonlinear manifold of face images when the illumination conditions change frequently. Replacing Euclidean distances by the geodesic distances, a novel nearest geodesic distances classifier and a nearest feature Line classifier using geodesic distances are presented. It has been shown from the experimental results that the two novel classifiers have higher recognition rates than the traditional classifiers.