Use of Gradient and Normal Vectors for Face Recognition

The main objective of this study is to compare face recognition accuracies in the case when the grey levels in each pixel of the face images are replaced by the gradient and the surface normal vectors. Extensive information is provided to explain the differences between the gradient and the proposed features. Some well-known face recognition methods, such as common vector approach (CVA), discriminative CVA, and support vector machines are applied to the well-known databases of AR and Yale for comparison other than introducing a new method what the authors called as Sum of Pixel Slope Similarities Approach. The authors’ experimental results are compared with the state-of-the-art methods to the best of their knowledge. In conclusion, their results imply that using the surface normal vectors rather than the gradient vectors in each pixel with no additional work on their elements gives better recognition rates.