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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
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
Andreu Y, Mollineda RA (2008) On the complementarity of face parts for gender recognition. In: Iberoamerican congress on pattern recognition, pp 252–260. Springer
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
Baluja S, Rowley HA (2007) Boosting sex identification performance. Int J Comput Vision 71(1):111–119
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
Berbar MA (2014) Three robust features extraction approaches for facial gender classification. Vis Comput 30(1):19–31
Chapelle O, Vapnik V, Bousquet O, Mukherjee S (2002) Choosing multiple parameters for support vector machines. Mach Learn 46(1–3):131–159
Eidinger E, Enbar R, Hassner T (2014) Age and gender estimation of unfiltered faces. IEEE Trans Inf Forensics Secur 9(12):2170–2179
Faraji MR, Qi X (2015) Face recognition under illumination variations based on eight local directional patterns. IET Biom 4(1):10–17
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston
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
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
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
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
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
Li B, Lian X-C, Lu B-L (2012) Gender classification by combining clothing, hair and facial component classifiers. Neurocomputing 76(1):18–27
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
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
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
Moghaddam B, Yang M-H (2002) Learning gender with support faces. IEEE Trans Pattern Anal Mach Intell 24(5):707–711
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
Ozbudak O, Tukel M, Seker S (2010) Fast gender classification. In: IEEE international conference on computational intelligence and computing research, pp 1–5. IEEE
Patel B, Maheshwari R, Raman B (2016) Compass local binary patterns for gender recognition of facial photographs and sketches. Neurocomputing 218:203–215
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
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
Rai P, Khanna P (2010) Gender classification using radon and wavelet transforms. In: International conference on industrial and information systems, pp 448–451. IEEE
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
Roffo G, Melzi S, Cristani M (2015) Infinite feature selection. In: Proceedings of the IEEE international conference on computer vision, pp 4202–4210
Shih H-C (2013) Robust gender classification using a precise patch histogram. Pattern Recogn 46(2):519–528
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
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
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
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
Wang X, Tang X (2009) Face photo-sketch synthesis and recognition. IEEE Trans Pattern Anal Mach Intell 31(11):1955–1967
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
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
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
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
Zhong F, Zhang J (2013) Face recognition with enhanced local directional patterns. Neurocomputing 119:375–384
Conflict of interest
The authors have no conflict of interest with anybody involved in reviewing the paper and in carrying out the research work.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Communicated by V. Loia.
About this article
Cite this article
Bhattacharyya, A., Saini, R., Roy, P.P. et al. Recognizing gender from human facial regions using genetic algorithm. Soft Comput 23, 8085–8100 (2019). https://doi.org/10.1007/s00500-018-3446-9
- Facial gender recognition
- Facial landmark detection
- Combination of facial regions
- Genetic algorithm
- Decision fusion