Research On Applying Manifold Learning Algorithms To Face Recognition

Human face is the most common visual part. Human face recognition has become not only a hot research topic in the field of artificial intelligence and model recognition, but also a most potential recognition technology by biometric characteristic for the merits of being natural, directly perceived, safe and convenient. However, due to the complexity of human face structure, the diversity of face expression and the changeable in face image formation process, computer face recognition technology is universally considered a challenging study topic. In a sense, it is commonly accepted that human face is a manifold structure. Face dataset is a non-linear manifold formed by some inner variables, such as illumination condition, face pose and facial expression. If some controlled variables can be seeked, space dimensionality could be reduced greatly.In recent years, manifold learning become a hot study topic in the field of artificial intelligence and model recognition, and its aim is to seek low-dimensional smoothy manifold in high-dimensional observation data space. Since Roweis and Saul put forward LLE algorithm in 2000, Tenenbaum and his collegues proposed Isomap algorithm, especially after Donoho discovered Isomap algorithm can obtain the potential prameter space of face image manifold, Zhang Changshui and his collegues found LLE algorithm applied to face recognition can bring out better recognition effects, face recognition research based on manifold learning is attracting more and more attention. This dissertation focuses on the application of manifold learning in human face recognition, and proposed four novel algorithms based on manifold learning used for face recognition, and each algorithm's effectiveness has been verified by experiments.The major research work in the dissertation contains the following seven aspects:1.A brief introduction is made on some closely relative mathmatics theories used in manifold learning as the theoretic supporting which include topology manifold, differential manifold, Riemann manifold, geodesic, Hausdorff distance.2.A analysis is made on some important issues when manifold learning is applied to face recognition. 1)A brief account of manifold learning's research motive, technology support, as well as some mainstream algorithm's advantages and shortcomings. 2) Based on the analysis on the advantages and disvantages of main face recognition techniques, this dissertation put forward the possibility and feasibility of applying manifold learning to face recognition. 3) In accordance with face image features of high-dimensional data and non-structural, some key issues such as dimensionality reduction, dimensionality curses, data sparity, empty space phenommnnon and fat tailed distribution are studied. The differences and relations between manifold learning and dimensionality reduction, intrinsic dimensionality estimation, supervised learning, semi-supervised learning, supervised manifold learning, semi-supervised manifold learning are revealed from the perspective of mathematic pattern.3.Constructing a model and experimental platform to applying mainstream maniflod learning algorithms to face recognition. The possibility, advantages and dificiency are studied systematically through experiments. 1) face recognition experiment with linear manifold learning algorithm: PCA, LDA, LPP. 2) face recognition experiments with non-linear manifold learning, algorithm: Isomap, LLE, LE, LTSA.4.Because manifold learning can't make full use of sample's information and can't remove the unnecessary image information. Isomap algorithm requires a large quantity of training samples to describle non-linear manifold structure. However, face recognition itself is a small sample, for the lack of training samples, the recognition effects are lowered. This dissertation proposed a novel face recognition method. And its effects has been verified in open huamn face data base.5.LTSA is a famous manifold learning algorithm. However, during the process of model recognition, if two similar manifold modeds come together, a more complex manifold will be formed, where LTSA algorithm is difficult to process and classify the information. To solve the above problem, a new face recognition approach is proposed and the experimental results on open huamn face data base has showed its effectiveness.6. To solve the weakpoints which occur during the period of supervised and semi-supervised learning processing sample information, a novel face expression recognition approach is proposed based on semi-supervised manifold learning. In this approach, firstly, the distance between two points is adjusted to form distance matrix by nonlinear structure information and some label's information from face expression image data. Secondly, low-dimensionality discriminant property useful for face expression recognition can be extracted by reconstructing neighbouring linear and dimensionality reduction. Experimental results has indicated its effective performance.