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Image coding based on maximum entropy partitioning for identifying improbable intensities related to facial expressions

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Abstract

In this paper we investigate information-theoretic image coding techniques that assign longer codes to improbable, imprecise and non-distinct intensities in the image. The variable length coding techniques when applied to cropped facial images of subjects with different facial expressions, highlight the set of low probability intensities that characterize the facial expression such as the creases in the forehead, the widening of the eyes and the opening and closing of the mouth. A new coding scheme based on maximum entropy partitioning is proposed in our work, particularly to identify the improbable intensities related to different emotions. The improbable intensities when used as a mask decode the facial expression correctly, providing an effective platform for future emotion categorization experiments.

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References

  1. De la Torre Fernando and Jeffrey F Cohn 2011 Facial expression analysis. In: Visual analysis of humans, pp. 377–409. Springer London

  2. Kanade T, Cohn J F and Tian Y 2000 Comprehensive database for facial expression analysis. In: Proceedings of International Conference on Automatic Face and Gesture Recognition, pp. 46–53

  3. Zwakhalen Sandra M G, Jan P H Hamers, Huda H Abu-Saad and Martijn P F Berger 2006 Pain in elderly people with severe dementia: a systematic review of behavioural pain assessment tools. BMC Geriatr. 6(1): 3a

    Article  Google Scholar 

  4. Izard Carroll E et al 1983 Changes in facial expressions of 2-to 19-month-old infants following acute pain. Dev. Psychol. 19(3): 418

    Article  Google Scholar 

  5. Edwards Jane, Henry J Jackson and Philippa E Pattison 2002 Emotion recognition via facial expression and affective prosody in schizophrenia: a methodological review. Clin. Psychol. Rev. 22(6): 789–832

    Article  Google Scholar 

  6. Prkachin Kenneth M 1992 The consistency of facial expressions of pain: a comparison across modalities. Pain 51(3): 297–306

    Article  Google Scholar 

  7. Schwartz Gary E, Paul L Fair, Patricia Salt, Michel R Mandel and Gerald L Klerman 1976 Facial expression and imagery in depression: an electromyographic study. Psychosomatic Med. 38(5): 337–347

    Article  Google Scholar 

  8. Picard Rosalind W 2000 Toward computers that recognize and respond to user emotion. IBM Syst. J. 39(3.4): 705–719

  9. Bartlett Marian Stewart, Gwen Littlewort, Ian Fasel and Javier R Movellan 2003 Real time face detection and facial expression recognition: development and applications to human computer interaction. In: CVPRW’03, Conference on Comput.er Vision and Pattern Recognition Workshop 2003, vol. 5, pp. 53–53

  10. Fong Terrence, Illah Nourbakhsh and Kerstin Dautenhahn 2003 A survey of socially interactive robots. Robotics Autonom. Syst. 42(3): 143–166

    Article  MATH  Google Scholar 

  11. Scheirer Jocelyn, Raul Fernandez and Rosalind W Picard 1999 Expression glasses: a wearable device for facial expression recognition. In: CHI’99 Extended Abstracts on Human Factors in Computing Systems, pp. 262–263. ACM

  12. Lyons Michael J and Nobuji Tetsutani 2001 Facing the music: a facial action controlled musical interface. In: CHI’01 extended abstracts on Human factors in computing systems, pp. 309–310. ACM

  13. Den Uyl M J and Van Kuilenburg H 2005 The FaceReader: online facial expression recognition. In: Proceedings of Measuring Behavior, vol. 30

  14. Oliver Nuria, Alex Pentland and François Bérard 2000 LAFTER: a real-time face and lips tracker with facial expression recognition. Pattern Recognit. 33(8): 1369–1382

    Article  Google Scholar 

  15. Boucher Jerry D and Gary E Carlson 1980 Recognition of facial expression in three cultures. J. Cross-Cultural Psychol. 11(3): 263–280

    Article  Google Scholar 

  16. Tranel Daniel, Antonio R Damasio and Hanna Damasio 1988 Intact recognition of facial expression, gender, and age in patients with impaired recognition of face identity. Neurology 38(5): 690–690

    Article  Google Scholar 

  17. Cohen Ira, Nicu Sebe, Ashutosh Garg, Lawrence S Chen and Thomas S Huang 2003 Facial expression recognition from video sequences: temporal and static modeling. Comput. Vis. Image Understand. 91(1): 160–187

    Article  Google Scholar 

  18. Hills Peter J and Michael Pake J 2013 Eye-tracking the own-race bias in face recognition: revealing the perceptual and socio-cognitive mechanisms. Cognition 129(3): 586–597

    Article  Google Scholar 

  19. Lienhart Rainer and Jochen Maydt 2002 An extended set of haar-like features for rapid object detection. In: Proceedings of International Conference on Image Processing. 2002. vol. 1. IEEE

  20. Magalhães Filipe et al 2013 Compressive sensing based face detection without explicit image reconstruction using support vector machines. Image analysis and recognition. Springer Berlin Heidelberg, pp. 758–765

    Google Scholar 

  21. Reisfeld Daniel and Yehezkel Yeshurun 1992 Robust detection of facial features by generalized symmetry. In: International Conference on Pattern Recognition, pp. 117–117. IEEE Computer Society Press

  22. Roth Dan, Ming-Hsuan Yang and Narendra Ahuja 2000 A SNoW-based face detector. Urbana 51: 61801

    Google Scholar 

  23. Susan Seba and Pooja Kadyan 2013 A supervised fuzzy eye pair detection algorithm. In: 5th International Conference on Computational Intelligence and Communication Networks (CICN), 2013, pp. 306–310. IEEE

  24. Zhang Lun et al 2007 Face detection based on multi-block lbp representation. Advances in biometrics. Springer Berlin Heidelberg, pp. 11–18

    Google Scholar 

  25. Viola Paul and Michael J Jones 2004 Robust real-time face detection. Int. J. Comput. Vis. 57(2): 137–154

    Article  Google Scholar 

  26. Ojala T, Pietikaenen M, Maenepae T 2002 Multi-resolution gray scale and rotation invariant texture classification with LBP. IEEE Trans. Pattern Anal. Mach. Intell. 24(7): 971–987

    Article  Google Scholar 

  27. Zhao G and Matti Pietikäinen 2007 Dynamic texture recognition using local binary patterns with application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6): 915–928

    Article  Google Scholar 

  28. Hernández Benjamín, Gustavo Olague, Riad Hammoud, Leonardo Trujillo and Eva Romero 2007 Visual learning of texture descriptors for facial expression recognition in thermal imagery. Comput. Vis. Image Understand. 106(2): 258–269

    Article  Google Scholar 

  29. Liao Shu, Wei Fan, Albert CS Chung and Dit-Yan Yeung 2006 Facial expression recognition using advanced local binary patterns, tsallis entropies and global appearance features. In: IEEE International Conference on Image Processing, 2006 pp. 665–668

    Google Scholar 

  30. Lyons Michael, Shigeru Akamatsu, Miyuki Kamachi and Jiro Gyoba 1998 Coding facial expressions with gabor wavelets. In: Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998, pp. 200–205

    Article  Google Scholar 

  31. Manjunath Bangalore S and Wei-Ying Ma 1996 Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8): 837–842

    Article  Google Scholar 

  32. Seba Susan and Gitin Kakkar 2015 Decoding facial expressions using a new normalized similarity index. In 2015 Annual IEEE India Conference (INDICON), pp. 1–6. IEEE

  33. Milborrow Stephen and Fred Nicolls 2008 Locating facial features with an extended active shape model. In: Computer Vision–ECCV 2008, pp. 504–513. Springer Berlin Heidelberg

    Chapter  Google Scholar 

  34. Huffman David A 1952 A method for the construction of minimum redundancy codes. Proc. IRE 40(9): 1098–1101

    Article  MATH  Google Scholar 

  35. Fano Robert M and Wintringham W T 1961 Transmission of information. Phys. Today 14: 56

    Article  Google Scholar 

  36. Shannon C E 1948 A mathematical theory of communication. Bell Syst. Tech. J. 27: 379–423

    Article  MathSciNet  MATH  Google Scholar 

  37. Chanda Bhabatosh and Dwijesh Dutta Majumder 2004 Digital image processing and analysis. PHI Learning Pvt. Ltd

    Google Scholar 

  38. Jeon Byeung-woo, Jechang Jeong and Ju-ha Park 1996 Apparatus for variable-length coding and variable-length-decoding using a plurality of Huffman coding tables. U.S. Patent 5,528,628, issued June 18

  39. Takishima Yasuhiro, Masahiro Wada, and Hitomi Murakami 1995 Reversible variable length codes. IEEE Trans. Commun. 43(234): 158–162

    MATH  Google Scholar 

  40. Zhou Jiantao, Zhiqin Liang, Yan Chen and Oscar C Au 2007 Security analysis of multimedia encryption schemes based on multiple Huffman table. Signal Process. Lett. IEEE 14(3): 201–204

    Article  Google Scholar 

  41. Nag Amitava, Sushanta Biswas, Debasree Sarkar and Partha Pratim Sarkar 2011 A novel technique for image steganography based on DWT and Huffman encoding. Int. J. Comput. Sci. Security 4(6): 497–610

    Google Scholar 

  42. Al-Laham Mohammed and Ibrahiem MM El Emary 2007 Comparative study between various algorithms of data compression techniques. Int. J. Comput. Sci. Netw. Security 7(4): 281

    Google Scholar 

  43. Christiansen Mark M et al 2013 Brute force searching, the typical set and Guesswork. IEEE International Symposium on Information Theory Proceedings (ISIT), 2013

  44. Xiaohua Huang, Guoying Zhao, Xiaopeng Hong, Wenming Zheng, and Matti Pietikäinen 2016 Spontaneous facial micro-expression analysis using spatiotemporal completed local quantized patterns. Neurocomputing 175: 564–578

    Article  Google Scholar 

  45. Seba Susan and Roni Chakre 2016 3D-difference theoretic texture features for dynamic face recognition. In 2016 International conference on computational techniques in information and communication technologies (ICCTICT), pp. 227–232. IEEE

  46. Pal Nikhil R and Sankar K Pal 1989 Entropic thresholding. Signal Process. 16(2): 97–108

    Article  MathSciNet  Google Scholar 

  47. Susan Seba and Madasu Hanmandlu 2013a A non-extensive entropy feature and its application to texture classification. Neurocomputing 120: 214–225

    Article  Google Scholar 

  48. Seba Susan and Madasu Hanmandlu 2015 Unsupervised detection of nonlinearity in motion using weighted average of non-extensive entropies. SIViP 9(3): 511–525

    Article  Google Scholar 

  49. Susan S and Dwivedi M 2014 Dynamic growth of hidden-layer neurons using the non-extensive entropy. In 2014 Fourth international conference on communication systems and network technologies (CSNT), 7 Apr 2014, pp. 491–495. IEEE

  50. Seba Susan and Ankit Kumar 2016 Auto-segmentation using mean-shift and entropy analysis. In: 2016 3rd International conference on computing for sustainable global development (INDIACom), pp. 292–296. IEEE

  51. Susan Seba and Madasu Hanmandlu 2013 cDifference theoretic feature set for scale-, illumination-and rotation-invariant texture classification. IET Image Process. 7(8): 725–732

    Article  Google Scholar 

  52. Bezdek J C 1981 Pattern recognition with fuzzy objective function algorithms. New York: Plenum Press

    Book  MATH  Google Scholar 

  53. Raheja Jagdish Lal, Radhey Shyam, Jatin Gupta, Umesh Kumar and Bhanu Prasad P 2010 Facial gesture identification using lip contours. In: Second International Conference on Machine Learning and Computing (ICMLC), 2010, pp. 3–7. IEEE

  54. Engin Avci 2007 An expert system based on wavelet neural network adaptive norm entropy for scale invariant texture classification. Expert Syst. Appl. 32: 919–926

    Article  Google Scholar 

  55. Filko Damir and Goran Martinovic 2013 Emotion recognition system by a neural network based facial expression analysis. Automatika–J. Control Meas. Electron. Comput. Commun. 54(2): 263–272

    Article  Google Scholar 

  56. Shan Caifeng, Shaogang Gong and Peter W McOwan 2009 Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis. Comput. 27(6): 803–816

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Delhi Technological University, Delhi, 110042, India

    Seba Susan, Nandini Aggarwal, Shefali Chand & Ayush Gupta

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  1. Seba Susan
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  2. Nandini Aggarwal
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  4. Ayush Gupta
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Correspondence to Seba Susan.

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Susan, S., Aggarwal, N., Chand, S. et al. Image coding based on maximum entropy partitioning for identifying improbable intensities related to facial expressions. Sādhanā 41, 1393–1406 (2016). https://doi.org/10.1007/s12046-016-0559-7

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