Face recognition using eigen-faces, fisher-faces and neural networks

In this paper, a new face recognition method based on PCA (principal component analysis), LDA (linear discriminant analysis) and neural networks is proposed. This method consists of four steps: i) preprocessing, ii) dimension reduction using PCA, iii) feature extraction using LDA and iv) classification using neural network. Combination of PCA and LDA is used for improving the capability of LDA when a few samples of images are available and neural network classifier is used to reduce number misclassification caused by not-linearly separable classes. The proposed method was tested on Yale face database. Experimental results on this database demonstrated the effectiveness of the proposed method for face recognition with less misclassification in comparison with previous methods.