Facial Expression Recognition Based on Fusion Feature Extraction of DCT and LBP
Luo Yua · Bandaoti guangdian · 2014
To the problem that the discrete cosine transform(DCT)only can extract the global characteristics of facial expression while ignore the relationship between the adjacent pixels,so the texture feature information can not be extracted and similar expression can not be accurately distinguished.A novel method by fusing discrete cosine transform(DCT)and local binary pattern(LBP)features was proposed for expression recognition in this research.The primary information of the face image was centralized in a small number of DCT coefficients, which were used as the global feature of the expression.Then LBP was used to extract the local features of the mouth and eyes area,which contributes most to facial expression recognition. Fusing the global and local texture feature would be more effective for facial expression recognition.Finally,support vector machine(SVM)was used to make feature fusion so as to complete facial expression recognition.Experimental results indicate that this method is better than individual DCT,which improves the accuracy of expression recognition,and can be used in the control of intelligent wheelchair successfully.