Abstract:Most of existing facial expression recognition algorithms apply single feature operators, such as Local Phase Quantization(LPQ),Local Binary Patterns(LBP),Histograms Of Oriented Gradients(HOG),and Gabor, but the recognition rate usually is not satisfactory under the condition of complex illumination. To improve the recognition rate, this paper proposes a new multi-feature-fusion facial expression recognition algorithm conducted in color space. Firstly, the proposed algorithm extracts multiple features, LPQ, LBP, HOG and Gabor in different color spaces, and thereafter extracts linear discriminating features and conducts classification using Nearest Neighbor Classifiers. Finally, the algorithm applies Adaboost algorithm to fuse the multiple classifiers for improving accuracy. The proposed algorithm is verified using Multi-PIE and achieves the average accuracy of 88.30%, showing that multiple feature fusion algorithm using color information largely enhances the accuracy of facial expression recognition and outperforms gray-based single feature methods evidently.