Enhanced Face Recognition based on PCA and SVM

Abstract

Feature Extraction and classification are important aspects of pattern recognition, computer vision. Principal Component Analysis is a well-known feature extraction and data representation technique. But this method is affected by illumination conditions. The combination o PCA an SVM for face recognition is presented in this paper. Before applying Principal Component Analysis preprocessing o images done by using wavelet transform. After PCA is applied or feature extraction. Support Vector Machine is used or classification. Experiments based on using Indian face database. The new technique achieves better performance than using PCA only.

Patch Based Collaborative Representation with Gabor Feature and Measurement Matrix for Face Recognition

“…Generally, there are two types of pattern classification methods [4,5]: parametric methods and nonparametric methods. Parametric methods such as support vector machine (SVM) [6,7] center on how to learn the parameters of a hypothesis classification model from the training samples and then use them to identify the class labels of test samples. In contrast, the nonparametric methods, such as nearest neighbor (NN) [8] and nearest subspace (NS) [9], use the training samples directly to identify the class labels of test samples.…”