Total views : 294

Multi-Level Haar Wavelet based Facial Expression Recognition using Logistic Regression


  • Department of Computer Engineering, Charotar University of Science and Technology, Changa - 388421, Gujarat, India
  • Department of Computer Engineering, BVM Engineering College, Anand – 388120, Gujarat, India


Background/Objective: Facial expressions play an equally important role as verbal communication and tonal expressions. Recognition of facial expression is important in industrial automation, security, medical and many other fields. Methods/ Statistical Analysis: In this paper, we propose multilevel haar wavelet-based approach, which extracts appearance features from prominent face regions at two different scales. The approach first segments most informative geometric components such as eye, mouth, eyebrows etc. using the Viola-Jones cascade object detector. Haar features of segmented components are extracted. One vs. All logistic regression model is used for the classification. Haar features are simple to compute and they can effectively represent the signal in low dimension, yet preserves the energy of the signal. Findings: The performance of the proposed approach is tested on well-known CK, JAFFE and TFEID facial expression datasets, and it achieves 90.48%, 88.57% and 96.84% accuracy for the respective dataset.


Classification, Facial Expression Recognition, Haar Wavelet, Logistic Regression

Full Text:

 |  (PDF views: 321)


  • Mehrabian A. Communication without words. Psychol Today. 1968; 2:53–5.
  • Fasel B, Luettin J. Automatic facial expression analysis: A survey. Pattern Recognit. 2003; 36(1):259–75. Crossref
  • Darvin C. The Expression of the Emotions in Man and Animals. London: J Murray; 1872.
  • Ekman P, Friesen WV. Constants across cultures in the face and emotion. J Pers Soc Psychol. 1971; 17(2):124–9.Crossref
  • Ekman P, Friesen W. Facial action coding system: A technique for measurement of facial movement. Consult Psychol Press; 1978.
  • Suwa M, Sugie N, Fujimora K. A preliminary note on patterrn recognition of human emotional expression.International Joint Conference on Pattern Recognition; 1978. p. 408–10.
  • Corneanu CA, Marc O, Cohn JF, Sergio E. Survey on RGB, 3D, thermal and multimodal approaches for facial expression recognition: History, trends and affect-related applications. IEEE Trans Pattern Anal Mach Intell. 2016; 38(8):1548–68. Crossref
  • Zeng Z, Pantic M, Roisman GI, Huang TS. A survey of affect recognition methods: Audio, visual, and spontaneous expressions. IEEE Trans Pattern Anal Mach Intell. 2009; 31(1):39–58. Crossref
  • Shan C, Gong S, Mcowan PW. Facial expression recognition based on local binary patterns: A comprehensive study. Image Vis Computing. 2009; 27(6):803–16.Crossref
  • Lyons M, Akamatsu S. Coding facial expressions with gabor wavelets. 3rd IEEE Conf Autom Face Gesture Recognit; 1998. p. 200–5. Crossref
  • Wenfei G, Xiang C, Venkatesh YV, Huang D, Lin H. Facial expression recognition using radial encoding of local Gabor features and classifier synthesis. Pattern Recognit.2012; 45(1):80–91. Crossref
  • Liu WF, Wang ZF. Facial expression recognition based on fusion of multiple gabor features. 18th International Conference on Pattern Recognition; 2006. p. 536–9.
  • Wu T, Bartlett MS, Movellan JR. Facial expression recognition using Gabor Motion Energy filters. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; 2010. Crossref
  • Almaev TR, Valstar MF. Local Gabor Binary Patterns from three orthogonal planes for automatic facial expression recognition.Humaine Association Conference on Affective Computing and Intelligent Interaction; 2013. p. 356–61.Crossref
  • Senechal T, Rapp V, Salam H, Seguier R, Bailly K, Prevost L. Facial action recognition combining heterogeneous featuresvia multikernel learning. IEEE Trans Syst Man Cybern B Cybern. 2012; 42(4):993–1005. Crossref
  • Samad R, Sawada H. Edge-based facial feature extraction using Gabor Wavelet and Convolution Filters. IAPR Conference Mach Visual Application; 2011. p. 430–3.
  • Ojala T, Pietikainen M, Harwood D. A comparative study of texture measures with classification based on feature distributions.Pattern Recognit. 1996; 29(1):51–9. Crossref
  • Ojala T, Pietikainen, M, Maenpaa T. Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell. 2002; 24(7):971–87. Crossref
  • Moore S, Bowden R. Local Binary Patterns for multi-view facial expression recognition. Comput Vis Image Underst.2011; 115(4):541–58. Crossref
  • Hablani R, Chaudhari N, Tanwani S. Recognition of facial expressions using Local Binary Patterns of important facial parts. Int J Image Process. 2013; 7(2):163–70.
  • Huang D, Shan C, Ardabilian M, Wang Y, Chen L. Local Binary Patterns and its application to facial image analysis: A survey. IEEE Trans Syst Man Cybern Part C Applied Review. 2011; 41(6):765–81. Crossref
  • Yacoob Y, Davis LS. Recognizing human facial expressions from long image sequences using Optical Flow. IEEE Trans Pattern Anal Mach Intell .1996; 18(6):636–42. Crossref
  • Essa IA, Pentland AP. Coding, analysis, interpretation, and recognition of facial expressions. IEEE Transaction Pattern Analysis Machine Intelligence. 1997; 19(7):757–63.Crossref
  • Gao Y, Leung M, Hui SC, Tananda M. Facial expression recognition from Line-Based caricatures. IEEE Trans Syst Man, Cybern - Part ASystems Humans. 2003; 33(3):407–12.Crossref
  • Shih FY, Chuang C-F, Wang P. Performance comparisons of facial expression recognition: JAFFE database. International Journal Pattern Recognit Artificial Intelligence. 2008; 22(3):445–59. Crossref
  • Turk Ma, Pentland AP. Face recognition using eigenfaces.Journal of Cognitive Neuroscience. 1991; 3:72–86.
  • Belhumeur PN, Hespanha JP, Kriegman DJ. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transacion Pattern Analysis Machine Intelligence. 1997; 19(7):711–20. Crossref
  • Bartlett MS, Movellan JR, Sejnowski TJ. Face Recognition by Independent Component Analysis. IEEE Transaction Neural Networks. 2002; 13(6):1450–64. Crossref
  • Yang J, Zhang D, Frangi AF, Yang J. Two-dimensional PCA: A new approach to appearance-based face representation and recognition. IEEE Transaction Pattern Analysis Machine Intelligence. 2004; 26(1):131–7. Crossref
  • Oliveira L, Koerich A, Mansano M, Britto A. 2D principal component analysis for face and facial-expressionrecognition.Computer Science Engineering. 2011; 13:9–13.Crossref
  • Rahulamathavan Y, Phan RC-W, Chambers JA, Parish DJ.Facial expression recognition in the encrypted domain based on Local Fisher Discriminant Analysis. IEEE Transaction Affect Computer. 2013; 4(1):83–92. Crossref
  • Zhi R, Ruan Q. Facial expression recognition based on twodimensional discriminant locality preserving projections.Neurocomputing. 2008; 71(7):1730–4. Crossref
  • Wang X, Liu A, Zhang S. New facial expression recognition based on FSVM and KNN. Opt - Interntional Journal Light Electron Optics. 2015; 126(21):3132–4. Crossref
  • Owusu E, Zhan Y, Mao QR. A Neural-AdaBoost based facial expression recognition system. Experting System Application Elsevier Ltd. 2014; 41(7):3383–90. Crossref
  • Guo JM, Tseng SH, Wong K. Accurate facial landmark extraction. IEEE Signal Process Lett. 2016; 23(5):605–9.Crossref
  • Ahmed R, Meyer A, Konik H, Bouakaz S. Framework for reliable, real-time facial expression recognition for low resolution images. Pattern Recognit Lett. 2013; 34(10):1159–68.Crossref
  • Lim J, Kim Y, Paik J. Comparative analysis of wavelet-based scale iinvariant feature extraction using different wavelet bases. Communications in Computer and Information Science. 2009; 297–303. Crossref
  • Papageiou C, Poggio T. A trainable system for object detection.Int J Comput Vis. 2000; 38(1):15–33. Crossref
  • Stankovir RS, Falkowski BJ. The haar wavelet transform: Its status and achievements. Comput Electr Eng. 2003; 29(1):25–44. Crossref
  • Mallat SG. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transaction Pattern Analysis Machine Intelligence. 1989; 11(7):674–93.Crossref
  • Press S, Wilson S. Choosing between logistic regression and discriminant analysis. J Am Stat Association. 1978; 73(364):699–705. Crossref
  • Ekman P, Friesen WV. Facial action coding system: A technique for the measurement of facial movement. Palo Alto: Consulting Psychologists Press; 1998.
  • Kanade T, Cohn JF, Tian Y. Comprehensive database for facial expression analysis. 4th IEEE International Conference on Automatic Face and Gesture Recognition; France. 2000. p. 46–53. Crossref
  • Lyons MJ. Automatic classification of single facial images. IEEE Trans Pattern Anal Mach Intell. 1999; 21(12):1357–62. Crossref
  • Chen L-F, Yen Y-S. Taiwanese facial expression image database.National Yang-Ming University, Taipei, Taiwan: Brain Mapping Laboratory, Institute of Brain Science; 2007.
  • Bartlett MS, Littlewort G, Frank M, Lainscsek C, Fasel I, Movellan J. Recognizing facial expression: Machine learning and application to spontaneous behavior. IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2005; 2:568–73.Crossref
  • Tian Y. Evaluation of face resolution for expression analysis.CVPR Workshop on Face Processing in Video; 2004.
  • Yang P, Liu Q, Metaxas DN. Exploring facial expressions with compositional features. IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2010. p. 2638–44. Crossref
  • Littlewort G, Bartlett MS, Fasel I, Susskind J, Movellan J.Dynamics of facial expression extracted automatically from video. Image Vis Comput. 2006; 24(6):615–25. Crossref
  • Yeasin M, Bullot B, Sharma R. From facial expression to level of interest: A spatio-temporal approach. IEEE Conference on Computer Vision and Pattern Recognition; 2004. p. 2-922. Crossref
  • Ouyang Y, Sang N, Huang R. Robust automatic facial expression detection method based on sparse representation plus LBP map. Opt - Int J Light Electron Opt. 2013; 124(24):6827–33. Crossref
  • Zheng H, Geng X, Tao D, Jin Z. A multi-task model for simultaneous face identification and facial expression recognition. Neurocomputing Elsevier. 2016; 171:515–23. Crossref
  • Praseeda L, Sasikumar. Analysis of facial expression using Gabor and SVM. Int J Recent Trends Eng. 2009; 1(2):47–50.
  • Kumbhar M, Jadhav A, Patil M. Facial expression recognition based on image feature. Int J Comput Commun Eng. 2012; 1(2):117–9. Crossref
  • Zhao L. Facial expression recognition based on PCA and NMF. 7th World Congress on Intelligent Control and Automation. 2008. p. 6826–9.


  • »
  • »

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.