Robust hybrid framework for automatic facial expression recognition

  • Authors

    • Gunavathi H S Jain University, Bangalore
    • Siddappa M SSIT, Tumkur
    2018-04-17
    https://doi.org/10.14419/ijet.v7i2.10764
  • Compressive sensing, facial expressions, feature extraction, HOG and LBP.
  • Over the last few years, facial expression recognition is an active research field, which has an extensive range of applications in the area of social interaction, social intelligence, autism detection and Human-computer interaction. In this paper, a   robust hybrid framework is presented to recognize the facial expressions, which enhances the efficiency and speed of recognition system by extracting significant features of a face. In the proposed framework, feature representation and extraction are done by using Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). Later, the dimensionalities of the obtained features are reduced using Compressive Sensing (CS) algorithm and classified using multiclass SVM classifier. We investigated the performance of the proposed hybrid framework on two public databases such as CK+ and JAFFE data sets. The investigational results show that the proposed hybrid framework is a promising framework for recognizing and identifying facial expressions with varying illuminations and poses in real time.

  • References

    1. [1] Fernandes S & Bala J, “A comparative study on various state of the art face recognition techniques under varying facial expressionsâ€, International Arab Journal of Information Technology, Vol. 14, No. 2, (2017), pp 254-259, available online: http://umc.edu.dz/images/A-Comparative-Study-on-Various-State-of-the-Art-Face-Recognition-Techniques-under-Varying-Facial-Expressions.pdf

      [2] Ekman P, Donato G, Bartlett M, Hager J & Sejnowski T, “Classifying facial actionsâ€, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 10, (1999), pp 974-989, available online: https://dl.acm.org/citation.cfm?id =319249

      [3] Gunavathi HS & Siddappa M, “A survey of techniques for automatic facial expression recognitionâ€, International Journal of Emerging Technology and Advanced Engineering, Vol. 7, No.9, (2017), pp 650-655, available online: http://www.ijetae.com/files/Volume7Issue9/IJETAE_0917_96.pdf

      [4] Viola P & Jones M, “Robust real-time face detectionâ€, International Journal of Computer Vision, Vol. 57, No. 2, (2004), pp 137-154, available online: https://link.springer.com /article/10.1023/B:VISI.0000013087.49260.fb

      [5] Valstar MF, Mehu M, Jiang B, Pantic M & Scherer K, “Meta-analysis of the first facial expression recognition challengeâ€, IEEE Transactions on Systems, Man, and Cybernatics-B, Vol. 42, No. 4, (2012), pp 966-979, available online: http://ieeexplore.ieee.org/ abstract/document/6222016/

      [6] Choi HC, & Oh SY, “Realtime facial expression recognition using active appearance model and multilayer perceptronâ€, Proceeding of the International Joint Conference SICE-ICASE, Busan, Korea, Vol. 2, (2006), pp 18-21, available online: http://ieeexplore.ieee.org/ document/4108639/

      [7] Moore S & Bowden R, “Local binary patterns for multi-view facial expression recognitionâ€, Computer Vision and Image Understanding, Vol.115, No. 4, (2011), pp 541-558, available online: https://dl.acm.org/citation.cfm?id=1951262

      [8] Bartlett MS, Moverllan JR & Sejnowski TJ, “Face recognition by independent component analysisâ€, IEEE Transactions on Neural Networks, Vol. 13, No. 6, (2002), pp 1450-1464, available online:http://ieeexplore.ieee.org/document/1058079/

      [9] En.wikipedia.org. (2017). Principal component analysis. Retrieved December 10, 2017, from https://en.wikipedia.org /wiki/Principal _component_analysis

      [10] Yousefi S, Nguyen MP, Kehtarnavaz N & Cao Y, “Facial expression recognition based on diffeomorphic matchingâ€, Proceedings of 17th IEEE International Conference on Image Processing (ICIP). Hong Kong, China, (2010), pp 4549-4552, available online: https://uthsc.pure.elsevier.com/en/publications /facial-expression-recognition-based-on-diffeomorphic-matching

      [11] En.wikipedia.org. (2017). Hidden markov model. Retrieved December 10, 2017, from https://en.wikipedia.org/wiki/Hidden_ markov_model

      [12] Kotsia I, & Pitas I, “Facial expression recognition in image sequences using geometric deformation features and support vector machinesâ€, IEEE Transactions on Image Processing, Vol. 16, No. 1, (2007), pp 172-187, available online: http://www.eecs.qmul.ac.uk/~ioannisp/pubs/ecopies/kotsiatip.pdf

      [13] Ojala T, Pietikainen M, & Maenpaa T, “Multiresolution gray-scale and rotation invariant texture classiï¬cation with local binary patternsâ€, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, (2002), pp 971-987, available online: https://dl.acm.org/citation.cfm?id=628808

      [14] Lowe DG, “Object recognition from local scale-invariant featuresâ€, Proceeding of the International Conference on Computer Vision, Washington, USA, Vol. 2, (1999), pp 1150-1157, available online: https://dl.acm.org/citation.cfm?id= 851523

      [15] Lowe DG, “Distinctive omage features from scale-invariant keypointsâ€, International Journal of Computer Vision, Vol.60, No. 2, (2004), pp 91-110, available online: https://www.cs.ubc.ca/ ~lowe/papers/ijcv04.pdf

      [16] Baraniuk RG (2007), Compressive sensing [Lecture notes], IEEE Signal Processing Magazin, Vol. 24, No. 4, pp 118-121, available online: http://ieeexplore.ieee.org/abstract/document /4286571/

      [17] En.wikipedia.org. (2017). Compressed sensing. Retrieved December 10, 2017, from https://en.wikipedia.org/wiki/ Compressed_sensing

      [18] Lyons MJ, Akamatsu S, Kamachi M, Gyoba J & Budynek J, “The japanese female facial expression (JAFFE) databaseâ€, (1998), availble online: http://www.kasrl.org/jaffe.html

      [19] Lucey P, Cohn JK, Kanade T, Saragih J, Ambadar Z & Matthews I, “The extended Cohn-Kanade dataset (CK+) A complete dataset for action unit and emotion-specified expressionâ€, Proceedings of the Third International Workshop on CVPR for Human Communicative Behaviour Analysis(CVPR4HB), San Francisco, (2010), pp 94-101, available online: http://ieeexplore.ieee.org/document/5543262/

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  • How to Cite

    H S, G., & M, S. (2018). Robust hybrid framework for automatic facial expression recognition. International Journal of Engineering & Technology, 7(2), 568-572. https://doi.org/10.14419/ijet.v7i2.10764