Research On Applying Identification Of Manifold Learing 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 two 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 four 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. Constructing a model and experimental platform to applying mainstream manifold learning algorithms to linear and non-linear data analysis. The possibility, advantages and dificiency are studied systematically through experiments.①data analysis experiments with linear manifold learning algorithm: PCA, LDA, LPP.②data analysis experiments with non-linear manifold learning, algorithm: ISOMAP, LLE, LE, LTSA.3. 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 have been verified in open huamn face data base.4. LTSA is a famous manifold learning algorithm. However, during the process of model recognition, if two similar manifold models 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 human face data base have showed its effectiveness.