Research On Binary Pattern-based Face Recognition And Expression Recognition

Human's life and work is related increasingly to computer,so the relationship between human and computer becomes closer and closer.Furthermore,many kinds of robots come out and will appear increasingly around us.Human long for the natural and harmonious human-computer interaction(HCI),i.e.,computers firstly distinguish the mastership and then act according to recognizing host's expression.Hence,the thesis sets the fast and accurate HCI as the goal and aims at improving the accuracy and speed of the face recognition and expression recognition.The major research works and contributions of the thesis include:1) It proposed a fast face recognition approach called local binary pattern histogram projection(LBPHP).The method projects local binary pattern(LBP) histogram onto locality preserving projection(LPP) space and obtains the low dimensional LBPHP feature.It is fast to recognize new sample in the low dimensional space.The method has good accuracy in the light of LPP's powerful discriminative property.LBPHP method is superior to conventional method based on LBP not only on recognition speed but also on accuracy.It is prominent especially on large-scale face database and suitable for practical application e.g.,identity authentication.2) In the aspect of expression feature extraction,the conventional LBP operators have several disadvantages such as rather long histograms produced by them,lower discrimination and sensitivity to noise.Aimed at the problems,it proposed the centralized binary pattern(CBP) operator.CBP operator has several advantages:(1) CBP operator captures the gradient information by comparing pairs of neighbors, which not only improves its discrimination but also reduces significantly the feature's dimensionality.(2) Its discrimination is strengthened due to considering the center pixel point and giving it the largest weight.(3) The CBP feature extracted from image is more robust and more stable in the noisy situation.Moreover,for the purpose of increasing recognition accuracy,it introduced the center-based nearest neighbor classifier into expression recognition.This kind of classifier is superior to the nearest neighbor classifer.3) In order to increase the accuracy of expression recognition,it expanded CBP from the following aspects:(1) The gradient information was integrated into CBP.(2) Multi-scale CBP(MCBP) was proposed.(3) For the purpose of improving the robustness to small deformation of expressional images,it introduced image Euclidean distance(IMED) and embedded it in MCBP.MCBP-IMED is short for the approach of MCBP embedded with IMED.The feature extracted by using this method has advantages:lower dimension,very powerful discrimination,insensitivity to noise, robustness to small deformation.4) It proposed the centralized Gabor binary pattern(CGBP) histogram,which combined CBP and Gabor transform,and integrated the gradient information into CGBP histogram.In order to reflect accurately the underlying structure of expressional manifold,it put forward the supervised Laplacianfaces(SLAP) which combined the ideas of local method and supervised method.Furthermore,because of the close relationship between facial expression and feeling,it proposed the expressional space model which combined the continuity and scatter of expressional space.Baesd on the model,SLAP was applied to CGBP histogram with gradient information to recognize expression and analyse expression components.