Novel Feature Extraction for Face Recognition using Multiscale Principal Component Analysis

A method of face recognition based on multiscale principal component analysis (MSPCA) is presented in this paper. Initially face area is extracted from the given face image using Adaboost face detection algorithm. From the face area, regions of interest such as eyes, nose and mouth part are extracted by dividing it along horizontal and vertical directions. Then MSPCA is employed on these regions of interest to extract the features. Multiscale Principal Component Analysis (MSPCA) combines the ability of PCA to decor relate the variables by extracting a linear relationship with that of wavelet analysis to extract deterministic features and approximately decor relate the auto correlated measurements. MSPCA computes the principal component analysis (PCA) of the wavelet coefficients at each scale, followed by combining the results at relevant scales. K-Nearest Neighbor (k-NN) classifier is used for recognition. The proposed methodology exhibits better recognition rate when compared to conventional principal component analysis.