A novel hybrid approach based on principal component analysis and tolerance rough similarity for face identification

Face identification plays one of the most important roles in biometrics to recognize a person. However, face identification is very difficult because of variations in size, orientations, different illuminations, and face expressions. In this paper, a hybrid approach is proposed based on principal component analysis (PCA) and tolerance rough similarity (TRS) for face identification. This paper comprises of three steps. First, PCA has been used to extract the feature vector from face images (eigenvectors). Second, the tolerance rough set-based similarity is applied for face matching and finally, the test image is compared with lower and upper approximation of similarity values that were found using TRS. The proposed hybrid approach gives a better recognition rate compared to other standard techniques like Euclidean distance and cosine similarity. The proposed work is evaluated on three face databases namely OUR databases and ORL databases and Yale databases. The experimental result of the proposed PCA-TRS approach is compared with other standard classification techniques like support vector machine (SVM), multilayer perceptron (MLP), back propagation network (BPN) and simple decision tree (CART) to conclude that proposed approach is better for face identification because of high accuracy and minimum error rate.