Local and Semi-Global Feature-Correlative Techniques for Face Recognition

Face recognition is an interesting field of computer vision with many commercial and ‎scientific applications. It is considered as a very hot topic and challenging problem at the ‎moment. Many methods and techniques have been proposed and applied for this purpose, ‎such as neural networks, PCA, Gabor filtering, etc. Each approach has its weaknesses as ‎well as its points of strength. This paper introduces a highly efficient method for the ‎recognition of human faces in digital images using a new feature extraction method that ‎combines the global and local information in different views (poses) of facial images. ‎Feature extraction techniques are applied on the images (faces) based on Zernike moments ‎and structural similarity measure (SSIM) with local and semi-global blocks. Preprocessing ‎is carried out whenever needed, and numbers of measurements are derived. More ‎specifically, instead of the usual approach for applying statistics or structural methods ‎only, the proposed methodology integrates higher-order representation patterns extracted ‎by Zernike moments with a modified version of SSIM (M-SSIM). Individual measurements and metrics resulted from mixed SSIM and Zernike-based approaches give a powerful ‎recognition tool with great results. Experiments reveal that correlative Zernike vectors give ‎a better discriminant compared with using 2D correlation of the image itself. The ‎recognition rate using ORL Database of Faces reaches 98.75%, while using FEI ‎(Brazilian) Face Database we got 96.57%. The proposed approach is robust against ‎rotation and noise.‎ Keywords—Zernike Moments; Face Recognition; Structural Similarity