Multi-view Face Detection And Recognition In Complex Background

Automatic face detection and recognition is emerging as an active research topic in the areas of pattern recognition and computer vision. At least two reasons account for this trend: one is the availability of low cost cameras and rapid progress of powerful personal computers; another is the wide range of commercial and law enforcement applications. It is a challenging topic because natural face images are formed by the interaction of multiple factors which broaden the diversity of face images. Complex background and multi-view are key problems that have to be solved in real enviorment based face detection and recognition tasks.A systemic review of existing face detection and recognition algorithms are given in this paper. Some crucial problems are discussed, for examples, the example selection and multi-view problems of probably approximately correct learning model based face detection, frontal face alignment algorithm, view manifolds and generative face models for multi-view face recognition. The main achivements of this paper are summarized as follows.(1) To handle the computation resource constraints to the size of training example set, an embedded Bootstrap example selection algorithm is proposed for active machine learning from example. Through formulation, theoretical comparison analysis indicates that the embedded Bootstrap algorithm, using almost the same training time with the traditional Bootstrap, selects more utility examples to represent a potentially overwhelming quantity of training samples. Thus a more effective predictor can be trained. Furthermore, both algorithms are applied to the negative example selection for AdaBoost based face detection system. And the experimental results show that the embedded Bootstrap strategy outperforms the traditional Bootstrap, which agrees with the theoretical analysis. This algorithm adapts to a wide range example selection tasks.(2) Confronting with big view variation, the common features of certain human's face images are dramastically reduced. Therefor, a novel face detection tree based on cluster validity analysis and FloatBoost learning is proposed to accommodate the in-class variability of multi-view faces. The tree splitting procedure is realized through dividing face training examples into the optimal sub-clusters using the fuzzy c-means algorithm together with a new cluster validity function based on the modified partition fuzzy degree. Then each sub-cluster of face examples is conquered with the FloatBoost learning to construct branches in the node of the detection tree. During training, the proposed algorithm is much faster than the original detection tree. The experimental results illustrate that the proposed detection tree is more efficient than the original one while keeping its detection speed.(3) The face recognition performance is influnced by the facial feature alignment accuracy. In order to describe the facial feature accurately, a face alignment algorithm is proposed for sequential face images. According to the smoothness of face images, a fast fuzzy connected sequential image segmentation algorithm is proposed to segment the face region from complex background. By locating the important features on the face, face images are aligned automatically. Experimental results show that the recognition rates of the aligned faces are higher than that of the non-aligned ones. The proposed relative fuzzy connected interactive segmentation algorithm is robust to different noise models, and it can also improve the speed of segmentation than the original scale based one. The experimental results on medical images show satisfacting results.(4) Multi-view is one of the most challenging factors for face recognition because of the nonlinearity in view subspace. In this paper, tensor decomposition is applied to separate influential factors of multi-view face images for face modeling. Then to handle the nonlinearity in view subspace, a novel data-driven manifold is proposed to model view variation. In this way, a uniform multi-view face model is achieved to deal with the linearity in identity subspace as well as the nonlinearity in view subspace. Meanwhile, a parameter estimation method is developed to solve the view coordinate in the manifold and the identity coefficient automatically. The proposed model yields improved facial recognition rates around 12% against the traditional TensorFace.(5) A new multi-view face recognition method that extends a recently proposed nonlinear tensor decomposition technique is proposed. This technique provides a generative face model that can deal with both the linearity and nonlinearity in multi-view face images. Particularly, the effectiveness of three kinds of view manifold for multi-view face representation is studied in this paper. An EM-like algorithm is developed to estimate the identity and view factors iteratively. The new face generative model can successfully recognize face images captured under unseen views, and the experimental results provide the new method is superior to the traditional TensorFace based algorithm around 19%. This approach is also applicable to other hybrid linear-nonlinear multi-factor object modeling tasks.In this paper, the research scheme and experimental results of multi-view face detection, face alignment and multi-view face recognition in complex background are given. These research achivements enrich the theories and applications of face dection and recogniton tasks. In addition, the proposed example selection algorithm, fast and robust image segmentation algorithm, and multi-view face detection and recognition models are applicable to other related pattern recognition tasks.