Research On Learning-Based Face Recognition

Face recognition is a key technique among biometric identification technologies, and its most important components are face detection and recognition. The aim of face recognition is to detect faces from images or videos accurately and recognize their identities. This dissertation focuses on learning-based face recognition, including machine learning methods such as clustering, manifold and subspace learning. The contributions of the dissertation are:1) To deal with the problem of face detection, two methods are proposed based on skin detection, which are called integral projection—Gaussian curves and modified substractive clustering, corresponding to single face and muti-face detection. In the approach of integral projection—Gaussian curves, two curves are obtained by integral projecting the binary-image to X and Y axes respectively, from which Gaussian curves are calculated and then, an accurate face region is found rapidly through the solution of Gaussian equation. The modified clustering algorithm proposes a new definition of distance for multi-face detection, and its key parameters can be predetermined adaptively by statistical information of face objects in the image. Downsampling is employed to reduce the computation of clustering and speed up the process of the proposed method. Meanwhile, the proposed approach also implements well in location of moving objects in video sequence. In order to estimate the angle of pose accurately, a cost function is proposed. The methods of gradient descent and sub-global enumerating are employed to search for the angle of pose. By rotating the image with the estimated angle, the pose is calibrated. And then, the eye map and mouth map are constructed by their characteristics of chroma and lum in the candidate region. Consequently, eyes and mouth are extracted for face validation.2) Focusing on the 3 essentials of manifold learning in face recognition, namely, (1) how to construct the neighborhood graph; (2) which measure can be used to estimate the true distance between two face samples; (3) what is the suitable cost function for embedding into subspace, two novel learning algorithms are derived from the manifold learning, which is called center based neighborhood embedding(CNE) and discriminant vector angle embedding(DVAE). Unlike the classical methods such as local linear embedding(LLE) and local preserving projection(LPP), CNE is a supervised linear dimensionality reduction method. It first computes centers of all sample classes. The input of the weight function between two samples is replaced by center based neighborhood(CN) distance. Then, the high-dimensional data are embedded into a low-dimensional space with preserving the CN geometric structure. On the other hand, DVAE constructs a graph with both positive and negative edges. The measure in DVAE is the angle between two vectors instead of modulus in traditional methods. It can be exempted from the estimation of the parameter in heat weight function. When test sample is embedded into low-dimensional space, a classification called angle nearest neighbor is used for face recognition.3) In ordrer to free face recognition from feature extraction, a method called orthogonal complement faces (OC-faces) is presented. The method is based on the orthogonal decomposition theorem. Firstly, the Gram-Schmidt orthogonal transformation is performed on the original training data of each class. Secondly, the orthogonal basis of each class spans a corresponding subspace. Therefore, the query sample can be decomposed into the sum of two components which are the orthogonal projection of query sample onto the corresponding subspace and the orthogonal complement of the subspace, respectively. Furthermore, the norm of the orthogonal complement indicates the distance between the query sample and the subspace of each class, so it can be used for classification.4) In order to deal with the problem of face recognition with one sample per person, a method called sub-block principle component analysis (PCA) based on partitions of the sample is presented in this disstertation. It first divides the sample into a few sub-blocks which have equal size and are non-overlapping, and then treats all the sub-blocks as a new sample set. Finally, PCA is performed on all the sub-blocks so as to extract features. Classification is done according to the projection coefficients of sub-blocks of a person.