Automatic Gender Identification from Children Facial Images Using Texture Descriptors

  • Ayesha Iftikhar
  • Rehan Ashraf
  • Asim Saeed
  • Srinivas Alva
  • Rajmohan PardeshiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 922)


Soft biometric such as gender significantly works to enhance the performance of biometric systems and also having applications in human–computer interaction, content-based indexing and retrieval, demographic studies, security, and surveillance. Gender identification among adults is easy as compared to in children, due to similarity of facial skin texture and appearance of faces. In this paper, we have introduced a technique to identify/classify the gender from facial images of children. In our method, we have applied three basic steps namely preprocessing, feature extraction, and classification. In preprocessing stage, face detection and normalization are performed. To extract the powerful features, we have computed different texture descriptors and after feature extraction process and SVM is applied for classification purpose. We have achieved the encouraging results in our experiments.


Gender identification in children Facial images Texture descriptors Support vector machines Soft biometric 



We thank to the parents of children who given the consent to capture the facial data of their children, with known fact that data will be used in publication and facial images will reveal the identity of their child. We also thank to the children who took part in the process of database collection.


  1. 1.
    Nixon MS, Correia PL, Nasrollahi K, Moeslund TB, Hadid A, Tistarelli M (2015) On soft biometrics. Pattern Recognit Lett 68:218–230CrossRefGoogle Scholar
  2. 2.
    Malik T (2014) Technology in the service of development: the NADRA story. Essay, Center for Global Development, Washington, DCGoogle Scholar
  3. 3.
    Sadruddin MM (2011) Study on the important issues of child rights in Pakistan. Dialogue 6(1):14Google Scholar
  4. 4.
    Satta R, Galbally J, Satta R, Galbally J, Beslay L (2014) Children gender recognition under unconstrained conditions based on contextual information, July 2016Google Scholar
  5. 5.
    Li Y-J, Lai C, Wu H, Pan S-T, Lee S-J (2015) Gender classification from face images with local. Int J Ind Electron Electr Eng 3(11):15–17Google Scholar
  6. 6.
    Xu Z, Lu L, Shi P (2008) A hybrid approach to gender classification from face images. In: 19th international conference on pattern recognition, p 14Google Scholar
  7. 7.
    Amilia S, Sulistiyo MD, Dayawati RN (2015) Face image-based gender recognition using complex-valued neural network. In: 2015 3rd international conference on information and commununication technology ICoICT, pp 201–206Google Scholar
  8. 8.
    Andreu Y, Mollineda RA, Garcia-Sevilla P (2009) Gender recognition from a partial view of the face using local feature vectors. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics), LNCS 5524, 481–488Google Scholar
  9. 9.
    Moghaddam B, Yang MH (2002) Learning gender with support faces. IEEE Trans Pattern Anal Mach Intell 24(5):707–711CrossRefGoogle Scholar
  10. 10.
    Jain A, Huang J, Fang S (2005) Gender identification using frontal facial images. In: IEEE international conference on multimedia expo, ICME 2005, pp 1082–1085Google Scholar
  11. 11.
    Basak P, De S, Agarwal M, Malhotra A, Vatsa M, Singh R (2017) Multimodal biometric recognition for toddlers and pre-school children, pp 627–633Google Scholar
  12. 12.
    Tiwari S, Singh A, Singh S (2012) Integrating faces and soft-biometrics for newborn recognition. 2(2):201–209Google Scholar
  13. 13.
    Tiwari S, Singh SK (2012) Face recognition for newborns. IET Biometrics 1(4):200–208CrossRefGoogle Scholar
  14. 14.
    Mozaffari S, Behravan H, Akbari R (2010) Gender classification using single frontal image per person: combination of appearance and geometric based features. In: Proceedings of the international conference on pattern recognition, pp 1192–1195Google Scholar
  15. 15.
    Shan C (2012) Learning local binary patterns for gender classification on real-world face images. Pattern Recognit Lett 33(4):431–437CrossRefGoogle Scholar
  16. 16.
    Datta S. Gender identification from facial images using local texture based features. Retrieved on 11 Oct 2018
  17. 17.
    Yang J, Zhang D, Frangi AF, Yang JY (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137CrossRefGoogle Scholar
  18. 18.
    Nguyen H, Thi T, Huong N (2017) Gender classification by LPQ features from intensity and monogenic images, pp 96–100Google Scholar
  19. 19.
    Guo G, Dyer CR, Fu Y, Huang TS (2009) Is gender recognition affected by age? 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, Kyoto, 2009, pp 2032–2039.
  20. 20.
    Sandygulova A, Dragone M, OHare GMP (2014) Real-time adaptive child-robot interaction: age and gender determination of children based on 3D body metrics. In: 23rd IEEE international symposium on robot and human interactive communication human-robot co-existence adaptive interfaces system dly life, ther assist social engagement interact, pp 826–831Google Scholar
  21. 21.
    Dniz O, Bueno G, Salido J, De La Torre F (2011) Face recognition using histograms of oriented gradients. Pattern Recognit Lett 32(12):1598–1603CrossRefGoogle Scholar
  22. 22.
    Balamurugan V, Srinivasan M, Vijayanarayanan A (2012) A new face recognition technique using Gabor wavelet transform with back propagation neural network. Int J Comput Appl 50(3):41–46; Thomaz CE, Giraldi GA (2010) A new ranking method for principal components analysis and its application to face image analysis. Image Vis Comput 28(6):902–913Google Scholar
  23. 23.
    Tenorio EZ, Thomaz CE (2011) Analise multilinear discriminante de formas frontais de imagens 2D de face. In: Proceedings of the X Simposio Brasileiro de Automacao Inteligente SBAI, pp 266–271Google Scholar
  24. 24.
    Universidade Federal de Sao Joao del Rei (2011) Sao Joao del Rei, Minas Gerais, Brazil, 18–21 Sept 2011Google Scholar
  25. 25.
    Hadid A, Pietikainen M, Ahonen T (2004) A discriminative feature space for detecting and recognizing faces. In: Proceedings on 2004 IEEE computer society conference on computer vision pattern recognition, CVPR 2004, vol 2(i), pp 797–804Google Scholar
  26. 26.
    Dalal N, Triggs W (2004) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision pattern recognition, CVPR05, vol 1(3), pp 886–893Google Scholar
  27. 27.
    Zhao Y, Zhang Y, Cheng R, Wei D, Li G (2015) An enhanced histogram of oriented gradients for pedestrian detection. IEEE Intell Transp Syst Mag 7(3):29–38CrossRefGoogle Scholar
  28. 28.
    Mustapha S, Jalab HA (2013) Compact composite descriptors for content based image retrieval. In: Proceedings-2012 international conference on advanced computer science application and technologies, ACSAT 2012, pp 37–42Google Scholar
  29. 29.
    Jalab HA (2011) Image retrieval system based on color layout descriptor and Gabor filters. In: 2011 IEEE conference on open systems, ICOS 2011, pp 32–36Google Scholar
  30. 30.
    Zhang H, Jiang X (2015) A method using texture and color feature for content-based image retrieval. In: 2015 IEEE international conference on computer and communications, pp 122–127Google Scholar
  31. 31.
    Vapnik V (2013) The nature of statistical learning theory. Springer Science & Business Media, BerlinzbMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ayesha Iftikhar
    • 1
  • Rehan Ashraf
    • 1
  • Asim Saeed
    • 2
  • Srinivas Alva
    • 3
  • Rajmohan Pardeshi
    • 4
    Email author
  1. 1.Department of Computer ScienceNTUFaisalabadPakistan
  2. 2.Department of Computer ScienceBeijing Jiaotong UniversityBeijingChina
  3. 3.Department of Computer ScienceGujarat UniversityAhmedabadIndia
  4. 4.Department of Computer ScienceKarnatak CollegeBidarIndia

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