Research On Robust Facial Expression Recognition Theory And Method Under Non-uniform Illumination And Patrial Occlusion

Facial expression is an effective way for humans to express their emotions, which is animportant part of human-computer interaction and affective computing. At present, facialexpression recognition system has broad application prospects and practical value, which isan active topic in the field of artificial intelligence and pattern recognition in the world. It isvery diffcult to recognize facial expression due to the complexity and subtlety of humanfacial expression. As far as the whole recognition system, the technology of facial expressionrecognition has got rapid progress. However, the performance of the facial expressionrecognition system is not sufficient to practical application and there are still many problemsneed to be further researched. One of the most important reasons is that facial expression willbe influenced by many factors, such as illumination, occlusion, pose and so on. In this paper,we put the emphases of the research on robust facial expression recognition undernon-uniform illumination and partial occlusion in order to improve the accuracy androbustness of expression recognition arithmetic in static facial expression image. The mainresearch contents and innovative work in this paper are shown as follows:First, in order to extract the texture information of facial expression and different scaleinformation of different expression behavior, we use Gabor filters to extract the facialexpression features. The Gabor multi-orientation fused features are combined with blockhistogram to extract facial expressional features in order to overcome the disadvantage oftraditional Gabor filter bank, whose high-dimensional Gabor features are redundant and theglobal features representation capacity is poor. In order to extract the multi-orientationinformation and reduce the dimension of the features, two fusion rules are proposed to fusethe original Gabor features of the same scale into a single feature. At the same time, torepresent the global features effectively, the fused image is divided into severalnon-overlapping rectangular units, and the histogram of each unit is computed and combinedas facial expression features. Experimental results show that the method is effective for bothdimension reduction and recognition performance.Second, a novel illumination-robust facial expression recognition method is proposed by using symmetric bilinear model to overcome the disadvantage of traditional2-dimensionalimage illumination preprocessing methods that they can degrade the quality of input imageand worsen recognition performance. We separate the illumination and expressioninformation which in the facial expression image under unknown illumination through thebilinear model, and build the illumination subspace and expression subspace respectively inorder to analyze and process the illumination information and expression informationindependently. The illumination factors are separated from the training database and theexpression factor is separated from testing image with arbitrary illumination, then the testingimage is transformed into a number of expression images exhibiting different illumination oftraining database. Experimental results show that the proposed method is better than thetraditional illumination preprocessing methods in recognition performance.Third, a novel facial expression recognition method under partial occlusion is proposedbased on local Gabor features radial grid encoding strategy in order to extract effective localfeatures to represent facial expression robustly. It has been found, in neurophysiological andpsychovisual studies, that two neighboring cells (both in retina and visual cortex) usuallyhave overlapping receptive fields. Therefore, in our implementation, a facial expressionimage is first divided into several local blocks which have50%overlap, and then each blockis represented by multi-scale and multi-orientation Gabor features, the resulting Gabor featureare encoded using radial grids, imitating the structure of human visual cortex. The proposedfeatures extraction method has the advantage of Gabor filters, which can represent the texturefeatures effectively and can overcome the disadvantage of Gabor filters, whose outputs arehighly correlated with redundant information at neighboring pixels. Better recognition ratesare achieved in JAFFE database with eyes occlusion and mouth occlusion. Experimentalresults show that the proposed local features coding method is effective to facial expressionrecognition under partial occlusion.Forth, an expression classification method based on support vector machine with localsummation kernel is proposed to overcome the disadvantage of conventional support vectormachine with global kernel, which can not process local features and is not robust toocclusion. The proposed recognition method based on local features, however, is robust toocclusion because partial occlusion affects only specific local features. In order to processlocal features in support vector machine effectively, local kernels are applied to process localfeatures and the the summation of local kernels is used as the integration method. Theeffectiveness and robustness of the proposed method are validated by comparison with globalkernel based support vector machine. The recognition rate is high under large occlusion, whereas the recognition rate of global kernel based support vector machine decreasesdrastically.Finally, the main content of this dissertation is summarized, and the further researchesare discussed.