Face Recognition Using Simultaneous Discriminative Feature and Adaptive Weight Learning Based on Group Sparse Representation

To better address face recognition task with occlusions and illumination changes, this letter proposes a novel framework called simultaneous discriminative feature and adaptive weight learning (SDFAWL). Sepecifically, SDFAWL uses a novel unified objective function to simultaneously learn the discriminant features, adaptive feature weights, and classification coefficients. First, a discriminant feature projection is incorporated into group sparse representation model, which reduces the intra-class distance between training samples. Second, we integrate adaptive feature weights into our model to penalize the noisy pixels, which is simultaneously learned by our unified objective function. Besides, we derive an efficient algorithm to optimize the proposed objective function, where the simultaneously learning scheme can encourage obtained parameters combining better and decrease the information loss. Extensive experiments under different conditions including occlusion, random noise, and illumination changes are conducted on the famous Aleix Martinez and Robert Benavente and ExYale B database demonstrate the effectiveness of our model.