Research On Face Recognition Based On Energy Image And Nonlinear Coupled Metric Learning

Face Recognition has been a hot issue in the field of pattern recognition and machinevision. As a unique biometric feature, face has the characteristics of directness, uniqueness,convenience etc. But due to the plasticity, variability and influence of many factors in imagingprocess, the automatic face recognition becomes a challenging task. Especially in videomonitoring environment, the problems that the face pose change in uncontrolled environmentand the degradation of face image (low resolution and fuzzy) because of camera replacementor human motion have become one of the bottlenecks of face recognition.In this paper, the relevant research on multi-pose changes and face degradation has beencarried out. Multi-pose face recognition includes two types:(1) The plane rotation facerecognition which could be obtained by the normalization;(2) The face recognition in thecase of pitch change and vacillating change which can not be corrected by the normalization.To solve the problem of these two types of multi-pose face recognition, effective facerecognition methods were proposed respectively. For the degradation problem of facerecognition, we proposed the idea of nonlinear coupled metric. The nonlinear coupled metriccan be directly used for feature extraction and classification of different resolution images andblurred images. The main contents of this paper are as follows:Firstly, aiming at the problem of face plane rotation, specific solution was proposed.Firstly, an eyes location method combining the AdaBoost algorithm with block integralprojection was proposed. This method has realized the high precise location of eyes ininclined face. The eye region could be roughly estimated based on the AdaBoost faceclassifier and eye classifier, and then accurate positions of two eyes were located in the eyearea through block integral projection. After locating the eyes position, the calculation methodof the rotation angle was given when the geometric center of image was as the rotationreference point. We realized the face plane rotation correction. The proposed method hascertain significance for perfecting the image correction theory. Finally, a face recognitionmethod based on sub-pattern Gabor features fusion was proposed, and it can effectivelyextract the feature of corrected face image which were used to the classification and similaritycalculating.Secondly, aiming at the problem of pitch change and vacillating change in multi-posechanges which could not be overcome through geometric correction, we proposed a novelface representation method, which is face energy image (FEI). Then the generalized face energy image and narrow face energy image were defined. The advantages of face energyimage have been verified through theoretical proof. For the fuzzy problem of face energyimage, we carried out image preprocessing adopting improved Retinex image enhancementmethod. Finally, to solve the problem of feature redundancy, a supervised locality preservingprojection feature extraction method based on the maximum separation degree difference wasproposed, which could be used to extract the nonlinear manifold information and classinformation contained in face energy image.Then, on the basis of face energy image (face mean energy image), another new energyimage was proposed, which is face variance energy image (FVEI). Face mean energy imageand variance energy image describe the multi-pose face image from two different angles:mean and variance respectively. Because these two kinds of energy image have differentclassification effects, on the basis of these features, combining with the feature level fusionstrategy, we proposed an effective multi-pose face recognition method integrating mean andvariance energy image. This method can solve the difficulties of multi-pose face recognitionbetter.Finally, in this paper, the degradation problem of face recognition was regarded asmeasurement problem among elements of different data collection. In order to solve thismeasurement problem, a new coupled metric learning method based on supervised localitypreserving projection was proposed. Combined with kernel technique, the method wasextended to nonlinear situation, futher more the kernel coupled metric learning method basedon supervised locality preserving projection was proposed. Finally, these two proposedcoupled metric learning methods were respectively applied to deal with the degradationproblem of face recognition, and better recognition effect was obtained.