Image latent semantic analysis based face recognition with ensemble extreme learning machine
Chao Wang, Jucheng Yang, Yarui Chen, Cao Wu, Yanbin Jiao · 2014
To overcome the shortcomings of the traditional methods, this paper proposes a novel face recognition method based on the image latent semantic features and ensemble extreme learning machine. The image latent semantic analysis is to acquire the high-level features from the face image, which has good robustness to illumination and expression changes. The image latent features are extracted as fellows: firstly, we obtain the low-level features of face image by the feature extraction methods of Gabor filter, local ternary pattern (LTP), and invariant moments. Then, we build the feature-image matrix based on the low-level features, and decompose the matrix with two dimension matrix decomposition to get the image latent semantic features. Finally, the ensemble extreme learning machine is used to classify the latent semantic features, which combines lots of extreme learning machine to obtain a stable classifier. The experimental results show that our proposed algorithm is more effective when compared with other algorithms.