The SoC Design for Facial Expression Recognition with Adaptive Speedy Algorithm in Order to Camera System

Abstract:

Smart and emerging technologies require fast processing hardware and software fusion solutions. In this paper we propose SoC design for facial expression recognition with adaptive speedy algorithm. In this order a shape detection algorithm is implemented on conditional random fields. In this way the conditional random fields is calculated into speedy conditional random fields (SCRF). On this new fields shape detection algorithm become speedy. The detection algorithm is designed and implemented fully as hardware and the block architecture uses indexed register bank with 1 clock delay output and with minimum latency. The block fits in a Xilinx Vertex5 device requiring 45,110 LUTs and No camera data memory. Additionally S3C6410 embedded board with processing ARM-11 was attached to device to further operations. The pointing input device SoC supports the proposed facial expression recognition will be fabricated.

• Periodical: Applied Mechanics and Materials (Volume 378)
• Pages: 662-668
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.378.662
• Online since: August 2013
• Authors: In Jung Lee
• Keywords: Embedded Board, Facial Expression Recognition, Pointing Input Device, SoC

References

[1] Z. Ambadar, J. Schooler, and J. Cohn. Deciphering the enigmaticface: The importance of facial dynamics to interpretingsubtle facial expressions. Psychological Science, (2005).

DOI: 10.1111/j.0956-7976.2005.01548.x

[2] M. S. Bartlett, G. Littlewort, C. Lainscsek, I. Fasel, and J. Movellan. Machine learning methods for fully automatic recognition of facial expressions and facial actions. In Proc. IEEE Intl Conf. Systems, Man and Cybernetics, pages 592–597, (2004).

DOI: 10.1109/icsmc.2004.1398364

[3] J. N. Bassili. Facial motion in the perception of faces and of emotional expression. (1978).

[4] I. Cohen, A. Garg, and T. S. Huang. Emotion recognition from facial expressions using multilevel hmm. In Neural Information Processing Systems, (2000).

[5] I. Cohen, N. Sebe, L. Chen, A. Garg, and T. S. Huang. Facial expression recognition from video sequences: Temporal and static modelling. In Computer Vision and Image Understanding, pages 160–187, (2003).

DOI: 10.1016/s1077-3142(03)00081-x

[6] T. F. Cootes, G. J. Edwards, and C. J. Taylor. Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23: 681–685, (2001).

DOI: 10.1109/34.927467

[7] A. Kanaujia and D. N. Metaxas. Recognizing facial expressions by tracking feature shapes. In International Conference on Pattern Recognition, pages 33–38, (2006).

DOI: 10.1109/icpr.2006.976

[8] J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labelingsequence data. In Proceedings of the 18th International Conference on Machine Learning, pages 282–289, San Fransisco, 2001. Morgan Kaufmann.

[9] P. Lucey, J. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews. The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression. In CVPR4HB10, pages 94–101, (2010).

DOI: 10.1109/cvprw.2010.5543262

[10] A. McCallum. Efficiently inducing features of conditional random fields, (2003).

[11] L. -P. Morency, A. Quattoni, and T. Darrell. Latent-dynamic discriminative models for continuous gesture recognition. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, 0: 1–8, (2007).

DOI: 10.1109/cvpr.2007.383299

[12] M. Pantic and L. Rothkrantz. Automatic analysis of facial expressions: The state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12): 1424–1445, (2000).

DOI: 10.1109/34.895976

[13] L. R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. In Proceedings of the IEEE, pages 257–286, (1989).

DOI: 10.1109/5.18626

[14] N. Sebe, M. S. Lew, I. Cohen, Y. Sun, T. Gevers, and T. S. Huang. Authentic facial expression analysis. In IEEE International Conference on Automatic Face and Gesture Recognition, pages 517–522, (2004).

DOI: 10.1109/afgr.2004.1301585

[15] C. Shan, S. Gong, and P. W. McOwan. Facial expression recognition based on local binary patterns: A comprehensive study. Image Vision Comput., 27: 803–816, May (2009).

DOI: 10.1016/j.imavis.2008.08.005

[16] C. Sminchisescu, A. Kanaujia, Z. Li, and D. Metaxas. Conditional models for contextual human motion recognition. In In Intl Conf. on Computer Vision, pages 1808–1815, (2005).

DOI: 10.1109/iccv.2005.59

[17] M. B. Stegmann and D. D. Gomez. A brief introduction to statistical shape analysis, mar 2002. Images, annotations and data reports are placed in the enclosed zip-file.

[18] C. Sutton and A. Mccallum. Introduction to Conditional Random Fields for Relational Learning. MIT Press, (2006).

[19] Y. -L. Tian, T. Kanade, and J. Cohn. Recognizing lower face action units for facial expression analysis. In Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition (FG'00), pages 484 – 490, March (2000).

DOI: 10.1109/afgr.2000.840678

[20] H. M. Wallach. Conditional random fields: An introduction. Technical Report MS-CIS-04-21, University of Pennsylvania, Philadelphia, (2004).

[21] S. B. Wang, A. Quattoni, L. philippe Morency, D. Demirdjian, and T. Darrell. Hidden conditional random fields for gesture recognition. In Computer Vision and Pattern Recognition, pages 1521–1527, (2006).

DOI: 10.1109/cvpr.2006.132

[22] G. Zhao and M. Pietikainen. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29: 915–928, (2007).

DOI: 10.1109/tpami.2007.1110

[23] Y. Zhu, L. C. D. Silva, and C. C. Ko. Using moment invariants and hmm in facial expression recognition. Pattern Recognition Letters, 23(1-3): 83–91, (2002).

DOI: 10.1016/s0167-8655(01)00108-8