access icon free Gender recognition based on face image using reinforced local binary patterns

Gender recognition is a challenging and innovative research topic in the present sophisticated world of visual technology. This study proposes a system which can identify the gender based on face image. For finding the location of the face region, each input image is divided into overlapping blocks and Gabor features are extracted with different scale and orientations. Generate the enhanced feature, concatenate mean, standard deviation and skewness of Gabor features which are obtained from each block. For detecting face region, this feature is passed to ensemble classifier. To recognise the gender, reinforced local binary patterns are used to extract the facial local features. Adaboost algorithm is used to select and classify the discriminative features such as male or female. The authors’ experimental results on Labeled Faces in the Wild (LFW), FERET and Gallagher databases for face detection using Gabor features achieve 98, 98.5 and 96.5% accuracy, respectively. Moreover, the reinforced local binary patterns achieve the accuracy for gender classification as 97.08, 98.5 and 94.21% on the LFW, FERET and Gallagher databases, respectively. Both are achieving improved performance compared with other standard methodologies described in the literature.

Inspec keywords: face recognition; Gabor filters

Other keywords: gender recognition; FERET; Gabor features; Gallagher databases; face image; face detection; Adaboost algorithm; reinforced local binary patterns; labeled faces in the wild

Subjects: Image recognition; Computer vision and image processing techniques

References

    1. 1)
      • 1. Santarcangelo, V., Farinella, G.M., Battiato, S.: ‘Gender recognition: methods, dataset and results’. Int. Workshop on Video Analytics for Audience Measurement, Torino, 2015, pp. 16.
    2. 2)
      • 23. Gallagher, A.C., Chen, T.: ‘Understanding images of groups of people’. Proc. IEEE Conf. Computer Vision Pattern Recognition, 2009, pp. 256263.
    3. 3)
      • 29. Zhong, L., Liu, Q., Yang, P., et al: ‘Learning multiscale active facial patches for expression analysis’, IEEE Trans. Cybern., 2015, 45, (8), pp. 14991510.
    4. 4)
      • 2. Toews, M., Arbel, T.: ‘Detection, localization and sex classification of faces from arbitrary viewpoints and under occlusion’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, (9), pp. 15671581.
    5. 5)
      • 4. Brunelli, R., Poggio, T.: ‘HyberBF networks for gender classification’. Proc. DARPA Image Understanding Workshop, 1995, pp. 311314.
    6. 6)
      • 21. Huang, G., Ramesh, M., Berg, T., et al: ‘Labeled faces in the wild: a database for studying face recognition in unconstrained environments’. Technical Report, University of Massachusetts, Amherst, 2007, pp. 0749.
    7. 7)
      • 18. Friedman, J., Hastie, T., Tibshirani, R.: ‘Additive logistic regression: a statistical view of boosting’, Ann. Stat., 2000, 28, (2), pp. 337407.
    8. 8)
      • 28. Kyungjoong, J., Choi, J., Jang, G.: ‘Semi-local structure patterns for robust face detection’, IEEE Signal Process. Lett., 2015, 22, (9), pp. 14001403.
    9. 9)
      • 3. Moghaddam, B., Yang, M.H.: ‘Learning gender with support faces’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (5), pp. 707711.
    10. 10)
      • 7. Wu, M., Zhou, J., Sun, J.: ‘Multi-scale ICA texture pattern for gender recognition’, IET Electron. Lett., 2012, 48, (11), pp. 629631.
    11. 11)
      • 6. Shih, H.C.: ‘Robust gender classification using a precise patch histogram’, Pattern Recognit., 2013, 46, (2), pp. 519528.
    12. 12)
      • 20. Viola, P., Jones, M.: ‘Rapid object detection using a boosted cascade of simple features’. Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2001, pp. 511518.
    13. 13)
      • 11. Hong, L., Yuan, G., Can, W.: ‘Gender identification in unconstrained scenarios using self-similarity of gradients features’. IEEE ICIP, 2014.
    14. 14)
      • 27. Viola, P., Jones, M.: ‘Robust real-time face detection’, Int. J. Comput. Vis., 2004, pp. 137154.
    15. 15)
      • 9. Hamid, H., Amin, Z., Avishan, N., et al: ‘Gender classification based on fuzzy clustering and principal component analysis’, IET Comput. Vis., 2016, 10, (3), pp. 228233.
    16. 16)
      • 25. Edoardo, A., Marco, L.C., Marco, M.: ‘Probabilistic corner detection for facial feature extraction’. Image Analysis and Processing – ICIAP, 2009, vol. 5716, pp. 461470.
    17. 17)
      • 22. Phillips, P.J., Moon, H., Rizvi, S.V., et al: ‘The FERET evaluation methodology for face-recognition algorithms’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (10), pp. 10901104.
    18. 18)
      • 12. Eran, E., Roee, E., Tal, H.: ‘Age and gender estimation of unfiltered faces’, IEEE Trans. Inf. Forensics Sec., 2014, 9, (12), pp. 21702179.
    19. 19)
      • 33. Gil, L., Tal, H.: ‘Age and gender classification using convolutional neural networks’. IEEE Workshop on Analysis and Modeling of Faces and Gestures, at the Conf. on Computer Vision and Pattern Recognition, 2015.
    20. 20)
      • 19. Vezhnevets, A., Vezhnevets, V.: ‘Modest AdaBoost-teaching AdaBoost to generalize better’. Graphicon, Novosibirsk Akademgorodok, 2005.
    21. 21)
      • 17. Wu, B., Ai, H., Huang, C.: ‘Fast rotation invariant multi-view face detection based on RealAdaboost’. Proc. IEEE Int. Conf. on Automatic Face and Gesture Recognition, 2004, pp. 7984.
    22. 22)
      • 26. Ngoc-Son, V., Alice, C.: ‘Face recognition with patterns of oriented edge magnitudes’. Computer Vision – ECCV, 2010, vol. 6311, pp. 313326.
    23. 23)
      • 30. Alnajar, F., Shan, C., Gevers, T., et al: ‘Learning-based encoding with soft assignment for age estimation under unconstrained imaging conditions’, Image Vis. Comput., 2012, 30, (12), pp. 946953.
    24. 24)
      • 14. Juan, E., Claudio, A.: ‘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 Sec., 2013, 8, (3), pp. 488499.
    25. 25)
      • 10. Darry, S., Adrian, P., Jianguo, Z.: ‘Gender classification via lips: static and dynamic features’, IET Biometrics, 2013, 2, (1), pp. 2834.
    26. 26)
      • 32. Jordi, M., Alberto, A., Roberto, P.: ‘Local deep neural networks for gender recognition’, Pattern Recognit. Lett., 2016, 70, pp. 8086.
    27. 27)
      • 31. Torrisi, A., Farinella, G.M., Puglisi, G., et al: ‘Selecting discriminative CLBP patterns for age estimation’. Int. Workshop on Video Analytics for Audience Measurement, Torino, 2015, pp. 16.
    28. 28)
      • 24. Pablo, D., Daniel, G., Long, Y., et al: ‘Single- and cross- database benchmarks for gender classification under unconstrained settings’. IEEE Int. Workshop on Benchmarking Facial Image Analysis Technologies, 2011.
    29. 29)
      • 13. Moeini, A., Faez, K., Moeini, H.: ‘Real-world gender classification via local Gabor binary pattern and three-dimensional face reconstruction by generic elastic model’, IET Image Process., 2015, 9, (8), pp. 690698.
    30. 30)
      • 16. Lee, T.S.: ‘Image representation using 2D Gabor wavelets’, IEEE Trans. PAMI, 1996, 18, (10), pp. 959971.
    31. 31)
      • 8. Jeffrey, B., Flora, D., Lochtefeld, F., et al: ‘Improved gender classification using nonpathological gait kinematics in full-motion video’, IEEE Trans. Hum.-Mach. Syst., 2015, 45, (3), pp. 304314.
    32. 32)
      • 15. Shan, C.: ‘Learning local binary patterns for gender classification on real-world face images’, Pattern Recognit. Lett., 2012, 33, (4), pp. 431437.
    33. 33)
      • 5. Lahoucine, B., Boulbaba, B.A., Mohamed, D.: ‘Boosting 3-D-geometric features for efficient face recognition and gender classification’, IEEE Trans. Inf. Forensics Sec., 2012, 7, (6), pp. 17661779.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2016.0087
Loading

Related content

content/journals/10.1049/iet-cvi.2016.0087
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading