Residual and Ensemble Learning on Locally Derived Features for Face Recognition

In this paper, an efficient approach toward face recognition is presented. The proposed model is invariant to pose, expression, scale, illumination, and translation with the application of different techniques and implementation of their algorithms. The images are first preprocessed using different techniques like CS-LBP, XCS-LBP, CS-LDMP, etc. to remove noise and make it illumination invariant. In one implementation, these are fed as input to different descriptors. The keypoints detected are invariant to lighting condition. A wide variety of descriptors are applied to the detected keypoints in order to compare the quality of features computed. These descriptors are quantized to a single vector, representing the most salient features of the facial image. The vectors are then fed into the stacking classifier. In the other method, these preprocessed images are directly fed as input into the residual network. A comparison of the results by these two pipelines is studied on two benchmark face datasets, namely FACES94 and Grimace.