Facial Expressions Recognition from Complex Background using Face Context and Adaptively Weighted sub-Pattern PCA
A new approach for facial expressions recognition based on face context and adaptively weighted sub-pattern PCA (Aw-SpPCA) has been presented in this paper. The facial region and others part of the body have been segmented from the complex environment based on skin color model. An algorithm has been proposed to accurate detection of face region from the segmented image based on constant ratio of height and width of face (δ= 1.618). The paper also discusses on new concept to detect the eye and mouth position. The desired part of the face has been cropped to analysis the expression of a person. Unlike PCA based on a whole image pattern, Aw-SpPCA operates directly on its sub patterns partitioned from an original whole pattern and separately extracts features from them. Aw-SpPCA can adaptively compute the contributions of each part and a classification task in order to enhance the robustness to both expression and illumination variations. Experiments on single standard face with five types of facial expression database shows that the proposed method is competitive.
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on Automatic Face and Gesture Recognition, 1998, S. 124-129.
[2] Heisele, B.; Poggio, T.; Pontil, M.: Face Detection in Still Gray Images,
in MIT AI Memo, AIM-1687, 2000.
[3] Yang, M.; Ahuja, M.; Kriegman, D.: Face Detection using a Mixture of
Factor Analyzers, in Proceedings of the International Conference on
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1999, S. I:274-280.
[5] Ekman, P.; Friesen, W.: The Facial Action Coding System: A Technique
for the Measurement of Facial Movement, in Consulting Psychologists Press, Palo Alto, CA, 1978.
[6] Tian, Y.; Kanade, T.; Cohn, J.: Recognizing Action Units for Facial
Expression Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Bd. 23, Nr. 2, 2001, S. 97-115.
[7] S. Gundimada, Li Tao, and v. Asari, "Face detection technique based on
intensity and skin color distribution," in 2004 International Conference
on Image Processing, Otc. 2004, vol. 2, pp. 1413-1416.
[8] K. P. Seng, A. Suwandy, and L.-M. Ang, "Improved automatic face
detection technique in color images," in IEEE Region 10 Conference TENCON 2004, Nov. 2004, vol. 1, pp. 459-462.
[9] Y. Araki, N. Shimada, and Y. Y. Shirai, "Detection of faces of various
directions in complex backgrounds," in 16th International Conference
on Pattern Recognition, 2002. Proceedings, Object 2002, vol. 1, pp. 409-412.
[10] S. Becker, M. Plumbley, Unsupervised neural network learning procedures for feature extraction and classification, J. Appl. Intell. 6 (3) (1996) 185-205.
[11] P.N. Belhumeur, J.P. Hespanha, D.J. Kriegman, Eigenfaces vs.
fisherfaces: recognition using class specific linear projection, IEEE
Trans. Pattern Anal. Machine Intell. 19 (7) (1997) 711-720.
[12] S.C. Chen, Y.L. Zhu, Subpattern-based principle component analysis,
Pattern Recogn. 37 (1) (2004) 1081-1083.
[13] R. Gottumukkal, V.K. Asari, An improved face recognition technique
based on modular PCAapproach, Pattern Recogn. Lett. 25 (4) (2004)
429-436.
[14] I.T. Jolliffe, Principal Component Analysis, second ed., Springer,
Berlin, New York, 2002.
[15] M. Kirby, L. Sirovich, Application of the KL procedure for the characterization of human faces,IEEE Trans. Pattern Anal. Machine
Intell. 12 (1) (1990) 103–108.
[16] A.M. Martinez, R. Benavente, The AR face database, CVC Technical
Report #24, June 1998.
[17] http://www.uk.research.att.com/facedatabase.html M. Turk, A.
Pentland, Eigenfaces for recognition, J. Cognitive Neurosci. 3 (1)
(1991) 71–86.
[1] Chai, D.; Ngan, K. N.: Locating Facial Regions of a Head-and-Shoulders Color Image, in Proceedings of the International Conference
on Automatic Face and Gesture Recognition, 1998, S. 124-129.
[2] Heisele, B.; Poggio, T.; Pontil, M.: Face Detection in Still Gray Images,
in MIT AI Memo, AIM-1687, 2000.
[3] Yang, M.; Ahuja, M.; Kriegman, D.: Face Detection using a Mixture of
Factor Analyzers, in Proceedings of the International Conference on
Image Processing, Bd. 3, 1999, S. 612-616.
[4] Jones, M.; Rehg, J.: Statistical Color Models with Application to Skin
Detection, in Proceedings of Computer Vision and Pattern Recognition,
1999, S. I:274-280.
[5] Ekman, P.; Friesen, W.: The Facial Action Coding System: A Technique
for the Measurement of Facial Movement, in Consulting Psychologists Press, Palo Alto, CA, 1978.
[6] Tian, Y.; Kanade, T.; Cohn, J.: Recognizing Action Units for Facial
Expression Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Bd. 23, Nr. 2, 2001, S. 97-115.
[7] S. Gundimada, Li Tao, and v. Asari, "Face detection technique based on
intensity and skin color distribution," in 2004 International Conference
on Image Processing, Otc. 2004, vol. 2, pp. 1413-1416.
[8] K. P. Seng, A. Suwandy, and L.-M. Ang, "Improved automatic face
detection technique in color images," in IEEE Region 10 Conference TENCON 2004, Nov. 2004, vol. 1, pp. 459-462.
[9] Y. Araki, N. Shimada, and Y. Y. Shirai, "Detection of faces of various
directions in complex backgrounds," in 16th International Conference
on Pattern Recognition, 2002. Proceedings, Object 2002, vol. 1, pp. 409-412.
[10] S. Becker, M. Plumbley, Unsupervised neural network learning procedures for feature extraction and classification, J. Appl. Intell. 6 (3) (1996) 185-205.
[11] P.N. Belhumeur, J.P. Hespanha, D.J. Kriegman, Eigenfaces vs.
fisherfaces: recognition using class specific linear projection, IEEE
Trans. Pattern Anal. Machine Intell. 19 (7) (1997) 711-720.
[12] S.C. Chen, Y.L. Zhu, Subpattern-based principle component analysis,
Pattern Recogn. 37 (1) (2004) 1081-1083.
[13] R. Gottumukkal, V.K. Asari, An improved face recognition technique
based on modular PCAapproach, Pattern Recogn. Lett. 25 (4) (2004)
429-436.
[14] I.T. Jolliffe, Principal Component Analysis, second ed., Springer,
Berlin, New York, 2002.
[15] M. Kirby, L. Sirovich, Application of the KL procedure for the characterization of human faces,IEEE Trans. Pattern Anal. Machine
Intell. 12 (1) (1990) 103–108.
[16] A.M. Martinez, R. Benavente, The AR face database, CVC Technical
Report #24, June 1998.
[17] http://www.uk.research.att.com/facedatabase.html M. Turk, A.
Pentland, Eigenfaces for recognition, J. Cognitive Neurosci. 3 (1)
(1991) 71–86.
@article{"International Journal of Information, Control and Computer Sciences:56621", author = "Md. Zahangir Alom and Mei-Lan Piao and Md. Ashraful Alam and Nam Kim and Jae-Hyeung Park", title = "Facial Expressions Recognition from Complex Background using Face Context and Adaptively Weighted sub-Pattern PCA", abstract = "A new approach for facial expressions recognition based on face context and adaptively weighted sub-pattern PCA (Aw-SpPCA) has been presented in this paper. The facial region and others part of the body have been segmented from the complex environment based on skin color model. An algorithm has been proposed to accurate detection of face region from the segmented image based on constant ratio of height and width of face (δ= 1.618). The paper also discusses on new concept to detect the eye and mouth position. The desired part of the face has been cropped to analysis the expression of a person. Unlike PCA based on a whole image pattern, Aw-SpPCA operates directly on its sub patterns partitioned from an original whole pattern and separately extracts features from them. Aw-SpPCA can adaptively compute the contributions of each part and a classification task in order to enhance the robustness to both expression and illumination variations. Experiments on single standard face with five types of facial expression database shows that the proposed method is competitive.
", keywords = "Aw-SpPC, Expressoin Recognition, Face context, Face Detection, PCA", volume = "6", number = "3", pages = "342-6", }
