Face recognition rate using different classifier methods based on PCA

This paper describes the different classifier methods with minimum means of clusters to achieve face recognition rate of humans from the feature extracted of training face image data for many sets of images as a data base. Principal Component Analysis (PCA) is a robust method used as feature extraction techniques for face recognition but the recognition decreases with the variation of person's actions. The features extracted for face images are light insensitive, individual, hidden, and activity effective to biometric recognition. The face recognition treats as two dimensions recognition problems, the fact is to take the advantage of these human faces are straight pose in general may be represented as a small set of two dimension characteristics view. The training and testing face images are selected from Research Laboratory for Olivetti and Oracle (ORL) face database, which have minimum pose variation. Three classifier methods are used to obtain the distance of recognition. These classifiers are: the Euclidian distance method, the Squared Euclidian Distance method, and the City-Block Distance method. By Clustering the difference of training image with images set for each person and determined the mean to it, the minimum mean is representing the recognition of the person. The cluster method with Squared Euclidian Distance method produces higher a recognition rate 100% near the Euclidian Distance method which gives a human face recognition rate 98% higher than the City-Block Distance method which gives a recognition rate 95%.