http://iet.metastore.ingenta.com
1887

Gender recognition based on face image using reinforced local binary patterns

Gender recognition based on face image using reinforced local binary patterns

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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.

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)
      • 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.
    3. 3)
      • 3. Moghaddam, B., Yang, M.H.: ‘Learning gender with support faces’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (5), pp. 707711.
    4. 4)
      • 4. Brunelli, R., Poggio, T.: ‘HyberBF networks for gender classification’. Proc. DARPA Image Understanding Workshop, 1995, pp. 311314.
    5. 5)
      • 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.
    6. 6)
      • 6. Shih, H.C.: ‘Robust gender classification using a precise patch histogram’, Pattern Recognit., 2013, 46, (2), pp. 519528.
    7. 7)
      • 7. Wu, M., Zhou, J., Sun, J.: ‘Multi-scale ICA texture pattern for gender recognition’, IET Electron. Lett., 2012, 48, (11), pp. 629631.
    8. 8)
      • 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.
    9. 9)
      • 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.
    10. 10)
      • 10. Darry, S., Adrian, P., Jianguo, Z.: ‘Gender classification via lips: static and dynamic features’, IET Biometrics, 2013, 2, (1), pp. 2834.
    11. 11)
      • 11. Hong, L., Yuan, G., Can, W.: ‘Gender identification in unconstrained scenarios using self-similarity of gradients features’. IEEE ICIP, 2014.
    12. 12)
      • 12. Eran, E., Roee, E., Tal, H.: ‘Age and gender estimation of unfiltered faces’, IEEE Trans. Inf. Forensics Sec., 2014, 9, (12), pp. 21702179.
    13. 13)
      • 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.
    14. 14)
      • 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.
    15. 15)
      • 15. Shan, C.: ‘Learning local binary patterns for gender classification on real-world face images’, Pattern Recognit. Lett., 2012, 33, (4), pp. 431437.
    16. 16)
      • 16. Lee, T.S.: ‘Image representation using 2D Gabor wavelets’, IEEE Trans. PAMI, 1996, 18, (10), pp. 959971.
    17. 17)
      • 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.
    18. 18)
      • 18. Friedman, J., Hastie, T., Tibshirani, R.: ‘Additive logistic regression: a statistical view of boosting’, Ann. Stat., 2000, 28, (2), pp. 337407.
    19. 19)
      • 19. Vezhnevets, A., Vezhnevets, V.: ‘Modest AdaBoost-teaching AdaBoost to generalize better’. Graphicon, Novosibirsk Akademgorodok, 2005.
    20. 20)
      • 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.
    21. 21)
      • 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.
    22. 22)
      • 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.
    23. 23)
      • 23. Gallagher, A.C., Chen, T.: ‘Understanding images of groups of people’. Proc. IEEE Conf. Computer Vision Pattern Recognition, 2009, pp. 256263.
    24. 24)
      • 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.
    25. 25)
      • 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.
    26. 26)
      • 26. Ngoc-Son, V., Alice, C.: ‘Face recognition with patterns of oriented edge magnitudes’. Computer Vision – ECCV, 2010, vol. 6311, pp. 313326.
    27. 27)
      • 27. Viola, P., Jones, M.: ‘Robust real-time face detection’, Int. J. Comput. Vis., 2004, pp. 137154.
    28. 28)
      • 28. Kyungjoong, J., Choi, J., Jang, G.: ‘Semi-local structure patterns for robust face detection’, IEEE Signal Process. Lett., 2015, 22, (9), pp. 14001403.
    29. 29)
      • 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.
    30. 30)
      • 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.
    31. 31)
      • 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.
    32. 32)
      • 32. Jordi, M., Alberto, A., Roberto, P.: ‘Local deep neural networks for gender recognition’, Pattern Recognit. Lett., 2016, 70, pp. 8086.
    33. 33)
      • 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.
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
This is a required field
Please enter a valid email address