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Facial expression recognition through modeling age-related spatial patterns

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Abstract

In this paper we tackle the problem of expression recognition by exploiting age-related spatial facial expression patterns, which carry crucial information that have not been thoroughly exploited. First, we conduct two statistic hypothesis tests to investigate age effect on the spatial patterns of expressions and on facial expression recognition respectively. Second, we propose two methods to recognize expressions by modeling age-related spatial facial expression patterns. One is a three-node Bayesian Network to classify expressions with the help of age from person-independent geometric features. The other is to construct multiple Bayesian networks to explicitly capture the spatial facial expression patterns for different ages. For both methods, age information is used as privileged information, which is only available during training, and is exploited during training to construct a better classifier. Statistic analyses on two benchmark databases, i.e. the Lifespan and the FACES, verify the age effect on spatial patterns of expressions and on facial expression recognition. Experimental results of expression recognition demonstrate the effectiveness of the proposed methods in modelling age-related spatial patterns as well as their superior expression recognition performance to existing approaches.

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References

  1. Alnajar F, Lou Z, Alvarez J, Gevers T Expression-invariant age estimation

  2. Bayramoglu N, Zhao G, Pietikainen M (2013) Cs-3dlbp and geometry based person independent 3d facial action unit detection. In: 2013 International Conference on Biometrics (ICB). IEEE, pp 1–6

  3. Bennett J, Fisher RA (1995) Statistical methods, experimental design, and scientific inference. Oxford University Press

  4. Bøttcher SG, Dethlefsen C Deal: a package for learning bayesian networks

  5. Cooper GF, Herskovits E (1992) A bayesian method for the induction of probabilistic networks from data. Mach Learn 9(4):309–347

    MATH  Google Scholar 

  6. Dibeklioglu H, Salah AA, Gevers T (2015) Recognition of genuine smiles. IEEE Trans Multimedia 17(3):279–294

    Article  Google Scholar 

  7. Ebner NC, Johnson MK (2010) Age-group differences in interference from young and older emotional faces. Cognition and Emotion 24(7):1095–1116

    Article  Google Scholar 

  8. Ebner NC, Riediger M, Lindenberger U (2010) Faces-a database of facial expressions in young, middle-aged, and older women and men: development and validation. Behav Res Methods 42(1):351–362

    Article  Google Scholar 

  9. Ekman P (1993) Facial expression and emotion. American psychologist 48(4):384

    Article  Google Scholar 

  10. Fölster M, Hess U, Werheid K (2014) Facial age affects emotional expression decoding. Frontiers in psychology, vol 5

  11. Guo G, Guo R, Li X (2013) Facial expression recognition influenced by human aging

  12. Heckerman D (2008) A tutorial on learning with bayesian networks. In: Innovations in Bayesian Networks. Springer, pp 33–82

  13. Hess U, Adams RB, Simard A, Stevenson MT, Kleck RE (2012) Smiling and sad wrinkles: Age-related changes in the face and the perception of emotions and intentions. J Exp Soc Psychol 48(6):1377–1380

    Article  Google Scholar 

  14. Houstis O, Kiliaridis S (2009) Gender and age differences in facial expressions. Eur J Orthod 31(5):459–466

    Article  Google Scholar 

  15. John PWM, John PW (1971) Statistical design and analysis of experiments. SIAM

  16. Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques. MIT press

  17. Lee Y, Lin Y, Wahba G (2001) Multicategory support vector machines. In: Proceedings of the 33rd Symposium on the Interface. Citeseer

  18. Lucey P, Cohn JF, Kanade T, Saragih J, Ambadar Z, Matthews I (2010) The extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, pp 94–101

  19. Minear M, Park DC (2004) A lifespan database of adult facial stimuli. Behav Res Methods Instrum Comput 36(4):630–633

    Article  Google Scholar 

  20. Pantic M, Rothkrantz LJM (2000) Automatic analysis of facial expressions: the state of the art. IEEE Trans Pattern Anal Mach Intell 22(12):1424–1445

    Article  Google Scholar 

  21. Shan W, Shangfei W, Jun W (2015) Enhanced facial expression recognition by age. FG2015 accepted

  22. Siegel S (1956) Nonparametric statistics for the behavioral sciences

  23. Vapnik V, Vashist A (2009) A new learning paradigm: learning using privileged information. Neural Netw 22(5):544–557

    Article  Google Scholar 

  24. Zeng Z, Pantic M, Roisman GI, Huang TS (2009) A survey of affect recognition methods: Audio, visual, and spontaneous expressions. IEEE Trans Pattern Anal Mach Intell 31(1):39–58

    Article  Google Scholar 

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Authors and Affiliations

  1. Key Lab of Computing and Communication Software of Anhui Province School of Computer Science and Technology, University of Science and Technology of China Hefei, Anhui, 230027, People’s Republic of China

    Shangfei Wang, Shan Wu & Zhen Gao

  2. Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute Troy, Troy, NY, 12180, USA

    Qiang Ji

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  1. Shangfei Wang
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  2. Shan Wu
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  3. Zhen Gao
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  4. Qiang Ji
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Correspondence to Shangfei Wang.

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Wang, S., Wu, S., Gao, Z. et al. Facial expression recognition through modeling age-related spatial patterns. Multimed Tools Appl 75, 3937–3954 (2016). https://doi.org/10.1007/s11042-015-3107-2

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  • DOI: https://doi.org/10.1007/s11042-015-3107-2

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