PCA, LDA and neural network for face identification

Algorithms based on principal component analysis (PCA) and subspace linear discriminant analysis (LDA) are popular in face recognition. PCA is used to perform dimension reduction on human face data and LDA creates another subspace to improve discriminant of PCA features. In this paper, we propose artificial neural networks (ANN) as an alternative to replace Euclidean distances in classification of human face features extracted by PCA and LDA. ANN is well recognized by its robustness and good learning ability. The algorithms were evaluated using the database of faces which comprises 40 subjects and with a total size of 400 images. Experimental results show that ANN reasonably improves the performance of PCA and LDA method. LDA-NN achieves an average recognition accuracy of 95.8%.