Robust Image Recognition Based on a New Supervised Kernel Subspace Learning Method

Fecha de lectura de Tesis Doctoral: 13 de septiembre 2019Image recognition is a term for computer technologies that can recognize certain people, objects or other targeted subjects through the use of algorithms and machine learning concepts. Face recognition is one of the most popular techniques to achieve the goal of figuring out the identity of a person. This study has been conducted to develop a new non-linear subspace learning method named “supervised kernel locality-based discriminant neighborhood embedding,” which performs data classification by learning an optimum embedded subspace from a principal high dimensional space. In this approach, not only is a nonlinear and complex variation of face images effectively represented using nonlinear kernel mapping, but local structure information of data from the same class and discriminant information from distinct classes are also simultaneously preserved to further improve final classification performance. Moreover, to evaluate the robustness of the proposed method, it was compared with several well-known pattern recognition methods through comprehensive experiments with six publicly accessible datasets. In this research, we particularly focus on face recognition however, two other types of databases rather than face databases are also applied to well investigate the implementation of our algorithm. Experimental results reveal that our method consistently outperforms its competitors across a wide range of dimensionality on all the datasets. SKLDNE method has reached 100 percent of recognition rate for Tn=17 on the Sheffield, 9 on the Yale, 8 on the ORL, 7 on the Finger vein and 11on the Finger Knuckle respectively, while the results are much lower for other methods. This demonstrates the robustness and effectiveness of the proposed method

Face Recognition using a Newly Developed Linear Subspace Learning Method

Face recognition is considered a specific physiological biometric in order to identify an individual according to the physical features of the human face. Much research has been conducted in such areas, but still more accurate processes are required for biometric facial recognition. This article presents a novel linear subspace learning method for face recognition which not only can take advantage of principle components analysis (PCA) as a successful feature extraction, but also can apply nearest local centroid mean vector (LMKNCN) as an effective classifier to improve the classification performance. The main goal of this particular scheme is to handle two common existing issues in recognition techniques: named sensitivity to the training sample size and negative effects of outliers. Moreover, to illustrate the performance of proposed developed PCA, we compare it with the latest dimensionality reduction techniques such as traditional PCA and KPCA on publicly available face dataset. Experimental results illustrate that our newly developed method has significantly achieved better performance over the same face database compared with the former KNN-based algorithms

Robust Face Recognition Based on a New Supervised Kernel Subspace Learning Method

Face recognition is one of the most popular techniques to achieve the goal of figuring out the identity of a person. This study has been conducted to develop a new non-linear subspace learning method named “supervised kernel locality-based discriminant neighborhood embedding,„ which performs data classification by learning an optimum embedded subspace from a principal high dimensional space. In this approach, not only nonlinear and complex variation of face images is effectively represented using nonlinear kernel mapping, but local structure information of data from the same class and discriminant information from distinct classes are also simultaneously preserved to further improve final classification performance. Moreover, in order to evaluate the robustness of the proposed method, it was compared with several well-known pattern recognition methods through comprehensive experiments with six publicly accessible datasets. Experiment results reveal that our method consistently outperforms its competitors, which demonstrates strong potential to be implemented in many real-world systems