Weighted SIFT Feature Learning with Hamming Distance for Face Recognition

Scale-invariant feature transform (SIFT) feature has been successfully utilized for face recognition for its tolerance to the changes of image scaling, rotation and distortion. However, a big concern on the use of original SIFT feature for face recognition is SIFT feature's high dimensionality which leads to slow image matching. Meanwhile, large memory capacity is required to store high dimensional SIFT features. Aiming to find an efficient approach to solve these issues, we propose a new integrated method for face recognition in this paper. The new method consists of two novel functional modules in which a projection function transforms the original SIFT features into a low dimensional Hamming feature space while each bit of the Hamming descriptor is ranked based on their discrimination power. Furthermore, a weighting function assigns different weights to the correctly matched features based on their matching times. Our proposed face recognition method has been applied on two benchmark facial image datasets: ORL and Yale datasets. The experimental results have shown that the new method is able to produce good image recognition rate with much improved computational speed.