access icon free Convolutional neural networks for gender prediction from smartphone-based ocular images

Automated gender prediction has drawn significant interest in numerous applications such as surveillance, human–computer interaction, anonymous customised advertisement system, image retrieval system, and biometrics. In the context of smartphone devices, gender information has been used to enhance the accuracy of the integrated biometric authentication and mobile healthcare system. Here, the authors thoroughly investigate gender prediction from ocular images acquired using front-facing cameras of smartphones. This is a new problem as previous research in this area has not explored RGB ocular images captured by smartphones. The authors used deep learning for the task. Specifically, pre-trained and custom convolutional neural network architectures have been implemented for gender prediction. Multi-classifier fusion has been used to improve the prediction accuracy. Further, evaluation of off-the-self-texture descriptors and study of human ability in gender prediction has been conducted for comparative analysis.

Inspec keywords: smart phones; human computer interaction; health care; feedforward neural nets; feature extraction; gender issues; image classification; image retrieval; image colour analysis; convolution; learning (artificial intelligence); neural net architecture; biometrics (access control)

Other keywords: smartphone devices; pre-trained network architectures; anonymous customised advertisement system; integrated biometric authentication; human–computer interaction; mobile healthcare system; smartphone-based ocular images; automated gender prediction; convolutional neural network architectures; gender information; image retrieval system

Subjects: Image recognition; Biology and medical computing; Knowledge engineering techniques; Computer vision and image processing techniques; Neural computing techniques; Information retrieval techniques; Mobile, ubiquitous and pervasive computing; User interfaces

References

    1. 1)
      • 20. Jain, A., Kanhangad, V.: ‘Investigating gender recognition in smartphones using accelerometer and gyroscope sensor readings’. Int. Conf. on Computational Techniques in Information and Communication Technologies, March 2016, pp. 597602.
    2. 2)
      • 7. Chen, C., Ross, A.: ‘Evaluation of gender classification methods on thermal and near-infrared face images’. Int. Joint Conf. on Biometrics, 2011, pp. 18.
    3. 3)
      • 25. Castrillón-Santana, M., Lorenzo-Navarro, J., Ramón-Balmaseda, E.: ‘On using periocular biometric for gender classification in the wild’, Pattern Recognit. Lett., 2016, 82, Part 2, pp. 181189. An insight on eye biometrics.
    4. 4)
      • 14. Dong, Y., Woodard, D.L.: ‘Eyebrow shape-based features for biometric recognition and gender classification: a feasibility study’. Int. Joint Conf. on Biometrics, October 2011, pp. 18.
    5. 5)
      • 15. Raja, K.B., Raghavendra, R., Vemuri, V.K., et al: ‘Smartphone based visible iris recognition using deep sparse filtering’, Pattern Recognit. Lett., Mob. Ir. Chall. Eval. I, 2015, 57, pp. 3342.
    6. 6)
      • 6. Moghaddam, B.: ‘Gender classification with support vector machines’. IEEE Int. Conf. on Automatic Face and Gesture Recognition, 2000, pp. 306311.
    7. 7)
      • 29. Dalal, N., Triggs, B.: ‘Histograms of oriented gradients for human detection’. Proc. of Int. Conf. on Computer Vision & Pattern Recognition, June 2005, vol. 2, pp. 886893.
    8. 8)
      • 34. Tapia, J.E., Perez, C.A., Bowyer, K.W.: ‘Gender classification from iris images using fusion of uniform local binary patterns’. Computer Vision – ECCV Workshops, 2015, pp. 751763.
    9. 9)
      • 13. Da Costa-Abreu, M., Fairhurst, M., Erbilek, M.: ‘Exploring gender prediction from iris biometrics’. Int. Conf. of the Biometrics Special Interest Group, September 2015, pp. 111.
    10. 10)
      • 21. Stawska, Z., Milczarski, P.: ‘Gender recognition methods useful in mobile authentication applications’, Inf. Syst. Manage., 2016, 5, (2), pp. 248259.
    11. 11)
      • 12. Omidiora, E.O., Ojo, O., Yekini, N.A., et al: ‘Analysis, design and implementation of human fingerprint patterns system “Towards age and gender determination, ridge thickness to valley thickness ratio (RTVTR) and ridge count on gender detection”’, Int. J. Adv. Res. Artif. Intell. (IJARAI), 2012, 1, (2), pp. 5763.
    12. 12)
      • 38. Russakovsky, O., Deng, J., Su, H., et al: ‘Imagenet large scale visual recognition challenge’, Int. J. Comput. Vis., 2015, 115, (3), pp. 211252.
    13. 13)
      • 19. Antal, M., Nemes, G.: ‘Gender recognition from mobile biometric data’. IEEE Int. Symp. on Applied Computational Intelligence and Informatics (SACI), May 2016, pp. 243248.
    14. 14)
      • 11. Gutiérrez-Redomero, E., Alonso, C., Romero, E., et al: ‘Variability of fingerprint ridge density in a sample of Spanish Caucasians and its application to sex determination’, Forensic Sci. Int., 2008, 180, pp. 1722.
    15. 15)
      • 24. Das, A., Pal, U., Ferrer, M.A., et al: ‘SSRBC 2016: sclera segmentation and recognition benchmarking competition’. Int. Conf. on Biometrics (ICB), June 2016, pp. 16.
    16. 16)
      • 8. Shan, C.: ‘Learning local binary patterns for gender classification on real-world face images’, Pattern Recognit. Lett., 2012, 33, (4), pp. 431437.
    17. 17)
      • 27. Merkow, J., Jou, B., Savvides, M.: ‘An exploration of gender identification using only the periocular region’. IEEE Int. Conf. on Biometrics: Theory, Applications and Systems, September 2010, pp. 15.
    18. 18)
      • 39. King, E.D.: ‘Dlib-ml: a machine learning toolkit’, J. Mach. Learn. Res., 2009, 10, pp. 17551758.
    19. 19)
      • 33. Tapia, J.E., Perez, C.A., Bowyer, K.W.: ‘Gender classification from the same iris code used for recognition’, IEEE Trans. Inf. Forensics Sec., 2016, 11, (8), pp. 17601770.
    20. 20)
      • 28. Ojala, T., Pietikäinen, M., Mäenpää, T.: ‘Multiresolution gray-scale and rotation invariant texture classification with local binary patterns’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (7), pp. 971987.
    21. 21)
      • 5. Makinen, E., Raisamo, R.: ‘Evaluation of gender classification methods with automatically detected and aligned faces’, IEEE Trans. Pattern Anal. Mach. Intell., 2008, 30, pp. 541547.
    22. 22)
      • 37. Haykin, S.S.: ‘Neural networks: a comprehensive foundation’ (Prentice Hall, Upper Saddle River, NJ, USA, 1999, 2nd. ed.).
    23. 23)
      • 22. Alhussein, M., Ali, Z., Imran, M., et al: ‘Automatic gender detection based on characteristics of vocal folds for mobile healthcare system’, Mob. Inf. Syst., 2016, 2016, pp. 135.
    24. 24)
      • 26. Kumari, S., Bakshi, S., Majhi, B.: ‘Periocular gender classification using global ICA features for poor quality images’, Procedia Eng., 2012, 38, pp. 945951.
    25. 25)
      • 17. Marsico, M.D., Nappi, M., Riccio, D., et al: ‘Mobile iris challenge evaluation (MICHE)-I, biometric iris dataset and protocols’, Pattern Recognit. Lett., 2015, 57, pp. 1723.
    26. 26)
      • 30. Kannala, J., Rahtu, E.: ‘BSIF: binarized statistical image features’. Int. Conf. on Pattern Recognition, November 2012, pp. 13631366.
    27. 27)
      • 32. He, K., Zhang, X., Ren, S., et al: ‘Deep residual learning for image recognition’, CoRR, 2015, abs/1512.03385.
    28. 28)
      • 35. Gallagher, A.C., Chen, T.: ‘Understanding images of groups of people’. IEEE Conf. on Computer Vision and Pattern Recognition, June 2009, pp. 256263.
    29. 29)
      • 23. Ahuja, K., Islam, R., Barbhuiya, F., et al: ‘Convolutional neural networks for ocular smartphone-based biometrics’, Pattern Recognit. Lett., 2017, 91, pp. 1726.
    30. 30)
      • 9. Acree, M.A.: ‘Is there a gender difference in fingerprint ridge density?’, Forensic Sci. Int., 1999, 102, (1), pp. 3544.
    31. 31)
      • 18. Rattani, A., Derakhshani, R., Saripalle, S.K., et al: ‘ICIP 2016 competition on mobile ocular biometric recognition’. IEEE Int. Conf. on Image Processing (ICIP), September 2016, pp. 320324.
    32. 32)
      • 36. Bansal, A., Agarwal, R., Sharma, R.K.: ‘SVM based gender classification using iris images’. Fourth Int. Conf. on Computational Intelligence and Communication Networks, November 2012, pp. 425429.
    33. 33)
      • 3. Rattani, A., Reddy, N., Derakhshani, R.: ‘Gender prediction from mobile ocular images: a feasibility study’. IEEE Symp. on Technologies for Homeland Security, Waltham, MA, 2017, pp. 16.
    34. 34)
      • 1. Cao, D., Chen, C., Piccirilli, M., et al: ‘Can facial metrology predict gender?’. Int. Joint Conf. on Biometrics, October 2011, pp. 18.
    35. 35)
      • 4. Bobeldyk, D., Ross, A.: ‘Iris or periocular? Exploring sex prediction from near infrared ocular images’. Int. Conf. of the Biometrics Special Interest Group, September 2016, pp. 17.
    36. 36)
      • 31. Simonyan, K., Zisserman, A.: ‘Very deep convolutional networks for large-scale image recognition’, CoRR, 2014, abs/1409.1556.
    37. 37)
      • 2. Moghaddam, B., Yang, M.: ‘Learning gender with support faces’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, pp. 707711.
    38. 38)
      • 16. Rattani, A., Derakhshani, R.: ‘Ocular biometrics in the visible spectrum: a survey’, Image Vis. Comput., 2017, 59, pp. 116.
    39. 39)
      • 10. Gungadin, S.: ‘Sex determination from fingerprint ridge density’, Internet J. Med. Update, 2007, 2, pp. 47.
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