A Classification of Emotion and Gender Using Local Biorthogonal Binary Pattern from Detailed Wavelet Coefficient Face Image

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 472)


This work investigates a framework which identifies gender and emotion of the person from the face image. Gender with their expressions has a vital role in the suspect detection systems. The proposed system aids in identification of a person with their gender as male and female. Also detects gender’s expression as joy and sadness. In this paper, wavelet detailed coefficient and Biorthogonal family-based system have been used simultaneously to identify gender and emotion of a face image. Detailed image local Biorthogonal binary pattern (DILBBP) has been applied for feature extraction and for classification purpose; SVM is applied. Experiments are performed on publicly available standard FERET, INDIAN FACE, and AR FACE databases. Proposed work gives acceptable classification and recognition results with less computational time.


Gender classification Emotion detection Detailed image local biorthogonal binary pattern (DILBBP) Support vector machine 


  1. 1.
    Tamura S, Kawai H, Mitsumoto H (1996) Male/female identification from 8 × 6 very low resolution face images by neural network. Pattern Recogn 29(2):331–335CrossRefGoogle Scholar
  2. 2.
    Baluja S, Rowley HA (2007) Boosting sex identification performance. Int J Comput Vision 71(1):111–119CrossRefGoogle Scholar
  3. 3.
    Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86CrossRefGoogle Scholar
  4. 4.
    Rowley H, Baluja S, Kanade T (1998) Neural network-based face detection. IEEE Trans Pattern Anal Mach Intel 20(1)Google Scholar
  5. 5.
    Osuna E, Freund R, Girosi F (1997) Training support vector machines: an application to face detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 130–136Google Scholar
  6. 6.
    Schneiderman H, Kanade T (1998) Probabilistic modeling of local appearance and spatial relationships for object recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 45–51Google Scholar
  7. 7.
    Viola P, Jones MJ (2001) Rapid object detection using a boosted cascade of simple features. In: Conference on computer vision and pattern recognition, vol 1, pp 8–14Google Scholar
  8. 8.
    Sandeep K, Rajagopalan AN (2002) Human face detection in cluttered color and edge information. In: The Indian conference on computer vision, graphics and image processing (ICVGIP)Google Scholar
  9. 9.
    Rajagopalan A, Kumar K, Karlekar J, Manivasakan R, Patil M, Desai U, Poonacha P, Chaudhuri S (1998) Finding faces in photographs. In: Proceedings of the 6th IEEE international conference on computer vision, pp 640–645Google Scholar
  10. 10.
    Saha S (2007) A symmetry based face detection technique. In: IEEE machine intelligence unitGoogle Scholar
  11. 11.
    Ekman P, Friesen WV (1978) Facial action coding system: investigator’s guide. Consulting Psychologists Press, Palo Alto, CAGoogle Scholar
  12. 12.
    Tian Y, Kanade T, Cohn J (2001) Recognizing action units for facial expression analysis. IEEE Trans Pattern Recogn Mach Intel 23(2):97–115. Carnegie-Mellon UniversityCrossRefGoogle Scholar
  13. 13.
    Mase K (1991) Recognition of facial expression from optical flow. IEICE Trans 3474–3483Google Scholar
  14. 14.
    Ozbudak O, Tukel M, Seker S (2010) Fast gender classification. In: IEEE international conference on computational intelligence and computing research (ICCIC), pp 1–5Google Scholar
  15. 15.
    Lyons MJ, Budynek J, Plante A, Akamatsu S (2000) Classifying facial attributes using a 2-d Gabor wavelet representation and discriminant analysis. In: Fourth IEEE international conference on automatic face and gesture recognition. IEEE, pp 202–207Google Scholar
  16. 16.
    Sun N, Zheng W, Sun C, Zou C, Zhao L (2006) Gender classification based on boosting local binary pattern. In: Advances in neural networks, Springer, pp 194–201CrossRefGoogle Scholar
  17. 17.
    Shan C (2012) Learning local binary patterns for gender classification on real world face images. Pattern Recogn Lett 33(4):431–437CrossRefGoogle Scholar
  18. 18.
    Craw L, Tock D, Bennett A (1996) Finding face features. In: Proceedings of the 2nd European conference on computer vision, pp 92–96CrossRefGoogle Scholar
  19. 19.
    Lanitis A, Taylor CJ, Cootes TF (1995) An automatic face identification system using flexible appearance models. Image Vis Comput 13(5):393–401CrossRefGoogle Scholar
  20. 20.
    Huchuan L, Huang Y, Chen Y, Yang D (2008) Automatic gender recognition based on pixel-pattern-based texture feature. J Real-Time Image Process (2008)Google Scholar
  21. 21.
    Saatci Y, Town C (2006) Cascaded classification of gender and facial expression using active appearance models. In: 7th IEEE international conference on automatic face and gesture recognition, pp 393–400Google Scholar
  22. 22.
    Pentland A, Moghaddan B, Starner T (1994), View based and modular Eigenspaces for face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 84–91Google Scholar
  23. 23.
    Yang M (2002) Detecting faces in images: a survey. IEEE Trans. Pattern Anal Mach Intell 24(1)Google Scholar
  24. 24.
    Yang G, Huang TS (1994) Human face detection in complex background. Pattern Recogn 27(1):53–63CrossRefGoogle Scholar
  25. 25.
    Shakhnarovich G, Viola P, Moghaddam B (2002) A unified learning framework for real time detection and classification. In: IEEE conference on AFGGoogle Scholar
  26. 26.
    Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Proceedings of the 13th international conference on machine learning, pp 148–156Google Scholar
  27. 27.
    Rai P, Khanna P (2015) An illumination, expression, and noise invariant gender classifier using two-directional 2DPCA on real Gabor space. J Vis Lang Comput 26:15–28CrossRefGoogle Scholar
  28. 28.
    Daubechies I et al (1992) Ten lectures on wavelets. SIAM 61Google Scholar
  29. 29.
    Ojala T, Pietikainen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recogn 29Google Scholar
  30. 30.
    Rahim MA, Hossain MN, Wahid T, Azam MS (2013) Face recognition using local binary patterns (LBP). Glob J Comput Sci Technol Graph Vis 13(4). version 1.0Google Scholar
  31. 31.
    Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167CrossRefGoogle Scholar
  32. 32.
    M. Sonka, V. Hlavac, R. Boyle, Digital image processing and computer vision, Cengage Learning, India edn (2008)Google Scholar
  33. 33.
    Vapnik VN (1998) Statistical learning theory. WileyGoogle Scholar
  34. 34.
    Phillips PJ, Wechsler H, Huang J, Rauss PJ (1998) The FERET database and evaluation procedure for face-recognition algorithms. Image Vis Comput 16(5):295–306CrossRefGoogle Scholar
  35. 35.
    Jain V, Mukherjee A (2002) The indian face databaseGoogle Scholar
  36. 36.
    Martinez AM (1998), The AR face database. CVC Technical ReportGoogle Scholar
  37. 37.
    Annalakshmi M, Roomi SMM, Priya SS (2016) Gender recognition from face images using texture descriptors for human computer interaction. J Chem Pharm Sci 9(3)Google Scholar
  38. 38.
    Al Mashagba EF (2016) Real-time gender classification by face. Int J Adv Comput Sci Appl 7(3)Google Scholar
  39. 39.
    Ban KD, Kim J, Yoon H (2016) Gender classification of low-resolution facial image based on pixel classifier boosting. ETRI J 38(2)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceGGITSJabalpurIndia

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