Perbandingan Koefisien NMF dan Proyeksi Bilinear Space Sebagai Fitur pada Pengenalan Ekspresi Wajah Manusia


  • William Salim Bina Nusantara University



NMF, face expression recognition, bilinear space


NMF is one new developed method to make the part-based representation of non-negative data, such as human face image. NMF can reduce the dimension of high dimensional data such as multimedia data. In many researches,NMF can also used as a classification technique done by utilizing the extracted feature through NMF process. This article discusses about the classification technique of human face expression using NMF. This is done using NMF coeffisient and bilinear projection of face image. Some researches show the use of NMF coefficient in classification and some others use bilinear space projection. This research is conducted by simulating face espression recognition to the two available approaches and then comparing the accuracy and time efficiency aspect of the two methods. Through this research, it can be concluded that the use of NMF coefficient results in better accuracy compared to bilinear space projection, but bilinear space projection obtains better time efficiency.


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Benetos, E., Kotti, M., & Kotropoulos, C. (2006). Applying supervised classifiers based on nonnegative matrix factorization to musical instrument classification. IEEE International Conference on Multimedia and Expo, 2105-2108.

Black, M., & Yacoob, Y. (1995). Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion. Proc. International Conference on Computer Vision, 374-381.

Bociu, I., & Pitas, I. (2004). A new sparse image representationalgorithm applied to facialexpression recognition: machine learning for signal processing. Proceedings of the 2004 14th IEEE Signal Processing Society Workshop, 539-548.

Calder, A. J., Burton, A. M., Miller, P., Young, A. W., & Akamatsu, S. (2001). A principal component analysis of facial expressions. Vision Research, 41, 1179–1208.

Cohen, L., Sebe, N., Garg, A., Chen, L., & Huang, T. (2003). Facial expression recognition from video sequences: temporal and static modeling. Computer Vision and Image Understanding, 91(1-2), 160-187.

Cottrell, G., & Metcalfe, J. (1991). Face, gender and emotion recognition using holons. Advances in Neural Information Processing Systems, 3, 564–571.

Ekman, P., & Friesen, W.V. (1978). Facial Action Unit System: Investigator’s Guide. California: Consulting Psychologists Press.

Essa, I., & Pentland, A. (1997). Coding, analysis, interpretation, and recognition of facial expressions. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(7), 757-767.

Fasel, B., & Luettin, J. (2000). Recognition of asymmetric facial action unit activities and intensities. Proceedings of the International Conference on Pattern Recognition (ICPR 2000).

Lanitis, A., Taylor, C., & Cootes, T. (1995). A unified approach to coding and interpreting face images. Proc. International Conf. on Computer Vision 368-373.

Lee D.D., & Seung, H.S. (2001). Algorithms for non-negative matrix factorization. NIPS, 13, 556–562.

Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401, 788-791.

Li, S. Z., Hou, X. W., & Zhang, H. J. (2001). Learning spatially localized, parts-based representation. Int. Conf. Computer Vision and Pattern Recognition, 207–212.

Mase, K. (1991). Recognition of facial expression from optical flow. IEICE Trans, 74(10), 3474-3483.

Otsuka, T., & Ohya, J. (1997). Recognizing multiple persons’ facial expressions using HMM based on automatic extraction of significant frames from image sequences. Proc. Int. Conf. on Image Processing, 546-549.

Raheja, J. L., & Kumar, U. (2010). Human facial expression detection from detected in captured image using back propagation neural network. International Journal of Computer Science and Information Technology, 2(1), 116-123.

Tian, Y., Kanade, T., & Cohn, J. (2001). Recognizing action units for facial expression analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2), 97-116.






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