Robust Face Recognition With Kernelized Locality-Sensitive Group Sparsity Representation

In this paper, a novel joint sparse representation method is proposed for robust face recognition. We embed both group sparsity and kernelized locality-sensitive constraints into the framework of sparse representation. The group sparsity constraint is designed to utilize the grouped structure information in the training data. The local similarity between test and training data is measured in the kernel space instead of the Euclidian space. As a result, the embedded nonlinear information can be effectively captured, leading to a more discriminative representation. We show that, by integrating the kernelized local-sensitivity constraint and the group sparsity constraint, the embedded structure information can be better explored, and significant performance improvement can be achieved. On the one hand, experiments on the ORL, AR, extended Yale B, and LFW data sets verify the superiority of our method. On the other hand, experiments on two unconstrained data sets, the LFW and the IJB-A, show that the utilization of sparsity can improve recognition performance, especially on the data sets with large pose variation

Bi-Objective Nonnegative Matrix Factorization: Linear Versus Kernel-Based Models

Nonnegative matrix factorization (NMF) is a powerful class of feature extraction techniques that has been successfully applied in many fields, namely in signal and image processing. Current NMF techniques have been limited to a single-objective problem in either its linear or nonlinear kernel-based formulation. In this paper, we propose to revisit the NMF as a multi-objective problem, in particular a bi-objective one, where the objective functions defined in both input and feature spaces are taken into account. By taking the advantage of the sum-weighted method from the literature of multi-objective optimization, the proposed bi-objective NMF determines a set of nondominated, Pareto optimal, solutions instead of a single optimal decomposition. Moreover, the corresponding Pareto front is studied and approximated. Experimental results on unmixing real hyperspectral images confirm the efficiency of the proposed bi-objective NMF compared with the state-of-the-art methods

Facial Landmarks Detection and Expression Recognition in the Dark

Facial landmark detection has been widely adopted for body language analysis and facial identification task. A variety of facial landmark detectors have been proposed in different approaches, such as AAM, AdaBoost, LBF and DPM. However, most detectors were trained and tested on high resolution images with controlled environments. Recent study has focused on robust landmark detectors and obtained increasing excellent performance under different poses and light conditions. However, it remains an open question about implementing facial landmark detection in extremely dark images. Our implementation is to build an application for facial expression analysis in extremely dark environments by landmarks. To address this problem, we explored different dark image enhancement methods to facilitate landmark detection. And we designed landmark correct- ness methods to evaluate landmarks’ localization. This step guarantees the accuracy of expression recognition. Then, we analyzed the feature extraction methods, such as HOG, polar coordinate and landmarks’ distance, and normalization methods for facial expression recognition. Compared with the existing facial expression recognition system, our system is more robust in the dark environment, and performs very well in detecting happy and surprising