Efficient Low-rank Supported Extreme Learning Machine for Robust Face Recognition

Recently, deep learning based face recognition algorithms have achieved great success in recognition performance. However, designing and training complex learning models suffer from time and labor efficiency. In this paper, we propose a novel three-layer low-rank supported extreme learning machine (LSELM) algorithm to take advantage of both robust feature representation and fast classification for efficient recognition. Every given probe sample is first clustered into a sub-class spanned by linear representation. With this sub-class, low-rank and robust features that are insensitive to disguise, noise, variant expression or illumination are recovered. These discriminative features are then coded to support a forward neural network for efficient prediction. Experimental results show that LSELM is on par with other deep learning based face recognition algorithms in recognition performance but has less time complexity on both AR and extend Yale-B datasets.