Part-based PCA for Facial Feature Extraction and Classification

are even impossible for human beings to classify correctly.
As a result, the above two factors encumber the recognition
rates to some extent.
Images
Table 1 Incorrect classification examples
a) Sadness Surprise Disgust Surprise
b) Happiness Happiness Sadness Neutral
III. CONCLUSION
For a more immersive virtual environment human
computer interaction, applying multimodal sensory
information such as hand gesture, speech, sound, body
posture and facial expression is necessary. In this paper, our
research focuses on facial expression recognition to express
human affective states. We present part-based PCA for facial
feature extraction and apply a modified PCA reconstruction
method for expression classification. Part-based PCA is
proposed as to minimize the influence of individual
differences. In order to achieve part-based PCA, a novel
feature detection and extraction approach based on multi-step
integral projection is proposed. The features can be
accurately detected and located by multi-step integral
projection curves and PCA is applied in the detected area
instead of the whole face. To solve the problem that the
features extracted from PCA are not the best features suitable
for classification but for expressing the data set, we propose a
modified PCA reconstruction method. We divide the training
set into 7 classes and carry out PCA reconstruction on each
class independently. The expression can be recognized by
measuring the similarity between the input image and the
reconstructed images.
method based on class features or multiple discriminant analysis,” in
Proceedings of the International Conference on Image Processing, Vol.
1, 1999, pp. 648-652.
[11] M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of
Cognitive Neuroscience, Vol. 19, 1997, pp. 743-756.
[12] P.Viola, M. Jones, ‘‘Robust real-time object detection,’’ Cambridge
Research Laboratory Technical Report Series CRL2001/01, pp. 1-24,
2001
[13] P.Viola, M. Jones, ‘‘Robust real-time face detection,’’, International
Journal of Computer Vision 57(2), 137-154,2004
[14] K. Turkowski, Apple Computer, “Filters for Common Resampling
Tasks” Filters for Common Resampling , (Tasks 10 April 1990)
[15] L. Zhang. I. Chen, Z, Gao, W., “Mean-based fast median filter”, Journal
of Tsinghua University, 2004, Vol 44; Part 9, page 1157-1159
[16] J. Sumbera, “Histogram Equalization”, CS – 4802, Digital Image
Processing, Lab #2
[17] P N. Belhumeur , J. P. Hespanha , D. J. Kriegman, Eigenfacesvs.
Fisherfaces: Recognition Using Class Specific Linear Projection, IEEE
Transactions on Pattern Analysis and Machine Intelligence, v.19 n.7,
p.711-720, July 1997
[18] J. Shlens, A Tutorial on Principal Component Analysis, 2005
[19] A. J. Calder, A. M. Burdon, P. Miller, A. W. Yong, S. Akamatsu, “A
principal component analysis of facial expressions”, Vision Resrach
41 (2001) 1179-1208
[20] T.Kanade, “Picture Processing System by Computer Complex and
Recognition of Human Faces,” Ph.D. thesis, Kyoto University, Japan,
Nov. 1973.
[21] N. Otsu. “A threshold selection method from gray-level histograms” [J].
IEEE Trans on SMC, 1979, 9(1): 62~66
[22] G. G. Mateos, A. Ruiz, P. E. Lopez-de-Teruel. “Face Detection Using
Integral Projection Models,” Proceedings of the Joint IAPR
International Workshop on Structural, Syntactic, and Statistical
Pattern Recognition. 2002, pp. 644-653
[23] Z. H. Zhou, X. Geng “Projection functions for eye detection,” Pattern
Recognition 37 (2004) 1049-1056
[24] L. M. Borja, O. FuentesJ.D. Foley, “Object detection using image
reconstruction with PCA”, Image and Vision Computing 27 (2009) 2-9
[25] M. Lyons, J. Budynek, and S. Akamatsu, “Automatic classification of
single facial images,” IEEE Transactions on Pattern Analysis and
Machine Intelligence, Vol. 21, 1999, pp. 1357-1362.
IV. REFERENCES
[1] Q. Chen, Real-time Vision-based Hand Gesture Recognition Using
Haar-like Features, IMTC IEEE Vol., Issue, pp. 1-6,200
[2] M.Turk, Gesture Recognition in Handbook of Virtual Environment
Technology, Lawrence Erlbaum Associates, Inc., 2001
[3] K. Matsumura, Y. Nakamura, and K. Matsui, “Mathematical
representation and image generation of human faces by
metamorphosis,” Electron. Commun. Jpn., vol. 80, pp. 36–46, 1997.
[4] P. Ekman, “Facial expression and emotion,” Am. Psychol., vol. 48, pp.
384–392, 1993.
[5] P. Ekman., “Strong evidence for universals in facial expressions: a reply
to Russell’s mistaken critique,” Psychol. Bull., vol. 115, pp. 268–287,
1994.
[6] P. Ekman, “Emotions inside out. 130 years after Darwin’s “The
Expression of the Emotions in Man and Animal”,” Ann. NY Acad. Sci.,
vol. 1000, pp. 1–6, 2003.
[7] M. Pantic and L. J. M. Rothkrantz, “Automatic analysis of facial
expressions:the state of the art,” IEEE Trans. Pattern Anal. Mach. Intell.,
vol. 22, pp. 1424–1445, 2000.
[8] B. Fasel, J. Luettin. Automatic facial expression analysis: a survey.
Pattern Recognition. 2003, 36(1): 259-275.
[9] P. Ekman and W. V. Friesen, Emotion in the Human Face. Englewood
Cliffs, NJ: Prentice-Hall, 1975.
[10] T. Kruozumi, Y. Shinza, Y. T. Kurozumi, Y. Shinza, Y. Kenmochi, and
K. Kotani, “Facial individuality and expression analysis by eigenspace
104