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. Pre-processing 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