Modular Human Face Recognition Method based on Principal Component Analysis and Mahalanobis Distance

The principal component analysis (PCA) method is one of the well-known dimensional reduction methods, The PCA has many applications in big data analysis from various fields. PCA is an essential method for image processing that is used directly or after several stages of preprocessing and in combination with other methods. Face recognition methods based on principal component analysis have many applications in face detection and recognition. In this paper, we present a cost-effective algorithm for human face recognition based on principal component analysis, which combines the Mahalanobis distance with the PCA method, the ability to detect faces in the shortest possible time for low-quality and black and white images. The architect of this method is modular, and every part of it can be hybridized with other methods. The proposed method is expressed and discussed in terms of parameters for determining the complexity and computational efficiency. Overall, it can be said that the method presented compared to other methods can process images with very low resolution and color depth, is able to recognize the face based on the B&W images, has no need for robust and costly computer systems, has a modular structure, and customizable based on distance (For example, a 30 percent increase of recognition rate from 49 % to 79 % in some implementations).