1. Intelligent Control and Automation, 2008. WCICA 2008.

2. Introduction Principal Component Analysis (PCA) Method Non-negative Matrix Factorization (NMF) Method PCA-NMF Method Experiments Result and Analysis Conclusion

3. In this paper, we have detailed PCA and NMF, and applied them to feature extraction of facial expression images. We also try to process basic image matrix and weight matrix of PCA and make them as the initialization of NMF. The experiments demonstrate that the method has got a better recognition rate than PCA and NMF.

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7. Image 50x50 It is difficult to get 2500 eigenvectors and eigenvalues.

9. PCA basic images

12. NMF basic images

14. PCA-NMF basic imagesNMF basic images

15. anger disgust fear happy neutral sad surprise

16. The comparison of recognition rate for every expression ( The training set comprises 70 images and the test set of 70 images)

17. The comparison of recognition rate for every expression ( The training set comprises 70 images and the test set of 143 images)

18. The comparison of recognition rate for every expression ( The training set comprises 140 images and the test set of 73 images)

19. The discussion or r

20. The results of experiments demonstrate that NMF and PCA-NMF can outperform PCA. The best recognition rate of facial expression image is 93.72%. On the whole, our approach provides good recognition rates.