Robust and efficient face recognition via low-rank supported extreme learning machine
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Springer Science+Business Media, LLC 2017. Recently, face recognition algorithms have made great progress in various realworld applications, e.g., authentication and criminal investigation. Deep-learning offers an end-to-end paradigm for vision recognition tasks and achieves good performance. However, designing and training the complex network architecture are time-consuming and laborintensive. Moreover, under complex scenarios, illumination change, noise or occlusion in images degrade the performance of recognition algorithms. In order to ameliorate these issues, we propose an efficient three-layered low-rank supported extreme learning machine (LSELM) algorithm for face recognition which improves the recognition performance under complex scenarios with high efficiency. In the first layer, a given probe sample is clustered into certain training subspace as pre-clustering. In the second layer, with this subspace, a low-rank subspace of probe sample as robust feature which is insensitive to disguise, noise, variant expression or illumination will be recovered by low-rank decomposition. Furthermore, these low-rank discriminative features are coded to support training a forward neural network termed LSELM. Experimental results indicate that the proposed approach is on par with some deep-learning based face recognition algorithms on recognition performance but with less time complexity over some popular face datasets e.g., AR, Extend Yale-B, CMU PIE and LFW datasets.