Improved face recognition with accelerated robust features improved by means of mean shift k-means clustering

To improve the precision of heterogeneous face recognition model, a heterogeneous face recognition model method based on binary multilayer Gabor Extreme Learning Machine (GELM) is proposed in this paper. Firstly, a random weighted Gabor feature extraction scheme is proposed based on pixel weight. It propagates the locally geometric input image sub-block to the hidden node, and embeds the extracted Gabor feature to the hidden layer. Moreover, it conducts random weighting and sum using a group of Gabor kernels so as to realise convolution operation of non-linear activation function of the propagated pixel; then, it estimates the output layer by means of linear weighting that is similar to Extreme Learning Machine (ELM). At last, the performance of heterogeneous face recognition method of the proposed algorithm is verified through BERC VIS-TIR database and CASIA NIR-VIS 2.0 database.