Recognizing gender from human facial regions using genetic algorithm


Recently, recognition of gender from facial images has gained a lot of importance. There exist a handful of research work that focus on feature extraction to obtain gender-specific information from facial images. However, analyzing different facial regions and their fusion help in deciding the gender of a person from facial images. In this paper, we propose a new approach to identify gender from frontal facial images that is robust to background, illumination, intensity, and facial expression. In our framework, first the frontal face image is divided into a number of distinct regions based on facial landmark points that are obtained by the Chehra model proposed by Asthana et al. The model provides 49 facial landmark points covering different regions of the face, e.g., forehead, left eye, right eye, lips. Next, a face image is segmented into facial regions using landmark points and features are extracted from each region. The compass LBP feature, a variant of LBP feature, has been used in our framework to obtain discriminative gender-specific information. Following this, a support vector machine-based classifier has been used to compute the probability scores from each facial region. Finally, the classification scores obtained from individual regions are combined with a genetic algorithm-based learning to improve the overall classification accuracy. The experiments have been performed on popular face image datasets such as Adience, cFERET (color FERET), LFW and two sketch datasets, namely CUFS and CUFSF. Through experiments, we have observed that, the proposed method outperforms existing approaches.

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  1. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041

    Article  MATH  Google Scholar 

  2. Andreu Y, Mollineda RA (2008) On the complementarity of face parts for gender recognition. In: Iberoamerican congress on pattern recognition, pp 252–260. Springer

  3. Asthana A, Zafeiriou S, Cheng S, Pantic M (2014) Incremental face alignment in the wild. In: Proceedings of international conference on computer vision and pattern recognition, pp 1859–1866

  4. Baluja S, Rowley HA (2007) Boosting sex identification performance. Int J Comput Vision 71(1):111–119

    Article  Google Scholar 

  5. Bekios-Calfa J, Buenaposada JM, Baumela L (2011) Revisiting linear discriminant techniques in gender recognition. IEEE Trans Pattern Anal Mach Intell 33(4):858–864

    Article  Google Scholar 

  6. Berbar MA (2014) Three robust features extraction approaches for facial gender classification. Vis Comput 30(1):19–31

    Article  Google Scholar 

  7. Chapelle O, Vapnik V, Bousquet O, Mukherjee S (2002) Choosing multiple parameters for support vector machines. Mach Learn 46(1–3):131–159

    Article  MATH  Google Scholar 

  8. Eidinger E, Enbar R, Hassner T (2014) Age and gender estimation of unfiltered faces. IEEE Trans Inf Forensics Secur 9(12):2170–2179

    Article  Google Scholar 

  9. Faraji MR, Qi X (2015) Face recognition under illumination variations based on eight local directional patterns. IET Biom 4(1):10–17

    Article  Google Scholar 

  10. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston

    MATH  Google Scholar 

  11. Golomb BA, Lawrence DT, Sejnowski TJ (1990) Sexnet: a neural network identifies sex from human faces. In: Conference on neural information processing systems, vol 1, p 2

  12. Huang D, Wang Y, Wang Y (2007) A robust method for near infrared face recognition based on extended local binary pattern. In: Proceedings of the 3rd international conference on advances in visual computing, vol II, pp 437–446

  13. Huang G B, Ramesh M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07-49, University of Massachusetts, Amherst

  14. Jabid T, Kabir MH, Chae O (2010) Gender classification using local directional pattern (LDP). In: 20th International conference on pattern recognition, pp 2162–2165. IEEE

  15. Jain A, Huang J (2004) Integrating independent components and support vector machines for gender classification. In Proceedings of the 17th international conference on pattern recognition, vol 3, pp 558–561. IEEE

  16. Li B, Lian X-C, Lu B-L (2012) Gender classification by combining clothing, hair and facial component classifiers. Neurocomputing 76(1):18–27

    Article  Google Scholar 

  17. Lu H, Huang Y, Chen Y, Yang D (2008) Automatic gender recognition based on pixel-pattern-based texture feature. J Real-Time Image Process 3(1–2):109–116

    Article  Google Scholar 

  18. Lyons MJ, Budynek J, Plante A, Akamatsu S (2000) Classifying facial attributes using a 2-D Gabor wavelet representation and discriminant analysis. In: Fourth international conference on automatic face and gesture recognition, pp 202–207. IEEE

  19. Makinen E, Raisamo R (2008) Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Trans Pattern Anal Mach Intell 30(3):541–547

    Article  Google Scholar 

  20. Moghaddam B, Yang M-H (2002) Learning gender with support faces. IEEE Trans Pattern Anal Mach Intell 24(5):707–711

    Article  Google Scholar 

  21. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29(1):51–59

    Article  Google Scholar 

  22. Ozbudak O, Tukel M, Seker S (2010) Fast gender classification. In: IEEE international conference on computational intelligence and computing research, pp 1–5. IEEE

  23. Patel B, Maheshwari R, Raman B (2016) Compass local binary patterns for gender recognition of facial photographs and sketches. Neurocomputing 218:203–215

    Article  Google Scholar 

  24. Perez C, Tapia J, Estévez P, Held C (2012) Gender classification from face images using mutual information and feature fusion. Int J Optomechatron 6(1):92–119

    Article  Google Scholar 

  25. Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The feret evaluation methodology for face-recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22(10):1090–1104

    Article  Google Scholar 

  26. Rai P, Khanna P (2010) Gender classification using radon and wavelet transforms. In: International conference on industrial and information systems, pp 448–451. IEEE

  27. Rai P, Khanna P (2014) A gender classification system robust to occlusion using Gabor features based (2D) 2 PCA. J Vis Commun Image Represent 25(5):1118–1129

    Article  Google Scholar 

  28. Roffo G, Melzi S, Cristani M (2015) Infinite feature selection. In: Proceedings of the IEEE international conference on computer vision, pp 4202–4210

  29. Shih H-C (2013) Robust gender classification using a precise patch histogram. Pattern Recogn 46(2):519–528

    MathSciNet  Article  Google Scholar 

  30. Tamura S, Kawai H, Mitsumoto H (1996) Male/female identification from \(8\times 6\) very low resolution face images by neural network. Pattern Recogn 29(2):331–335

    Article  Google Scholar 

  31. Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650

    MathSciNet  Article  MATH  Google Scholar 

  32. Tapia JE, Perez CA (2013) Gender classification based on fusion of different spatial scale features selected by mutual information from histogram of LBP, intensity, and shape. IEEE Trans Inf Forensics Secur 8(3):488–499

    Article  Google Scholar 

  33. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of international conference on computer vision and pattern recognition, vol 1, p I. IEEE

  34. Wang X, Tang X (2009) Face photo-sketch synthesis and recognition. IEEE Trans Pattern Anal Mach Intell 31(11):1955–1967

    Article  Google Scholar 

  35. Xie S, Shan S, Chen X, Chen J (2010) Fusing local patterns of Gabor magnitude and phase for face recognition. IEEE Trans Image Process 19(5):1349–1361

    MathSciNet  Article  MATH  Google Scholar 

  36. Zhang T, Tang YY, Fang B, Shang Z, Liu X (2009) Face recognition under varying illumination using gradientfaces. IEEE Trans Image Process 18(11):2599–2606

    MathSciNet  Article  MATH  Google Scholar 

  37. Zhang W, Shan S, Gao W, Chen X, Zhang H (2005) Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition. In Proceedings of the tenth IEEE international conference on computer vision, vol 1, pp 786–791

  38. Zheng J, Lu B-L (2011) A support vector machine classifier with automatic confidence and its application to gender classification. Neurocomputing 74(11):1926–1935

    Article  Google Scholar 

  39. Zhong F, Zhang J (2013) Face recognition with enhanced local directional patterns. Neurocomputing 119:375–384

    Article  Google Scholar 

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Correspondence to Debi Prosad Dogra.

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Bhattacharyya, A., Saini, R., Roy, P.P. et al. Recognizing gender from human facial regions using genetic algorithm. Soft Comput 23, 8085–8100 (2019).

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  • Facial gender recognition
  • Facial landmark detection
  • Combination of facial regions
  • Genetic algorithm
  • Decision fusion