Skip to main content
SpringerLink
Log in

The LFW-Gender Dataset

  • Conference paper
  • First Online:
Computer Vision – ACCV 2016 Workshops (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10118))

Included in the following conference series:

Abstract

Gender identification is a precursor for context specific emotion recognition. Small but significant differences have been noted across different gender groups in terms of emotion expressiveness. Apart from facial expressions and security applications, gender recognition is becoming increasingly relevant after the rise of applications involving social media platforms. Labelled Faces in the Wild (LFW) dataset is designed for studying the problem of face recognition under unconstrained environment. However, it is used to study other facial attributes as well, including gender. In this paper, we propose a standardized subset of LFW database (LFW-gender) that can be used as a benchmark for gender recognition algorithms. We also provide a baseline for performance on the dataset for gender recognition with various algorithms and some results may suggest that this is a harder subset to classify.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Baluja, S., Rowley, H.A.: Boosting sex identification performance. Int. J. Comput. Vis. 71, 111–119 (2007)

    Article  Google Scholar 

  2. Chaplin, T.M.: Gender and emotion expression: a developmental contextual perspective. Emot. Rev. 7, 14–21 (2015)

    Article  Google Scholar 

  3. Brody, L., Hall, J.: Gender and emotion in context. In: Lewis, M., Haviland-Jones, J.M., Barrett, L.F. (eds.) Handbook of Emotions, pp. 395–408. Guilford Press, New York (2010)

    Google Scholar 

  4. LaFrance, M., Hecht, M.A., Paluck, E.L.: The contingent smile: a meta-analysis of sex differences in smiling. Psychol. Bull. 129, 305 (2003)

    Article  Google Scholar 

  5. Allen, J.G., Haccoun, D.M.: Sex differences in emotionality: a multidimensional approach. Hum. Relat. 29, 711–722 (1976)

    Article  Google Scholar 

  6. Archer, J.: Sex differences in aggression in real-world settings: a meta-analytic review. Rev. Gen. Psychol. 8, 291 (2004)

    Article  Google Scholar 

  7. https://sites.google.com/site/usmantariq/

  8. Ng, C.B., Tay, Y.H., Goi, B.-M.: Recognizing human gender in computer vision: a survey. In: Anthony, P., Ishizuka, M., Lukose, D. (eds.) PRICAI 2012. LNCS (LNAI), vol. 7458, pp. 335–346. Springer, Heidelberg (2012). doi:10.1007/978-3-642-32695-0_31

    Chapter  Google Scholar 

  9. Martínez, A., Benavente, R.: The AR face database (1998)

    Google Scholar 

  10. Messer, K., Matas, J., Kittler, J., Luettin, J., Maitre, G.: Xm2vtsdb: the extended m2vts database. In: Second International Conference on Audio and Video-based Biometric Person Authentication, vol. 964, pp. 965–966. Citeseer (1999)

    Google Scholar 

  11. Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The feret evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1090–1104 (2000)

    Article  Google Scholar 

  12. Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the hausdorff distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001). doi:10.1007/3-540-45344-X_14

    Chapter  Google Scholar 

  13. Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression (PIE) database. In: 2002 Fifth IEEE International Conference on Automatic Face and Gesture Recognition, Proceedings, pp. 46–51. IEEE (2002)

    Google Scholar 

  14. Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 947–954. IEEE (2005)

    Google Scholar 

  15. Ricanek, K., Tesafaye, T.: MORPH: a longitudinal image database of normal adult age-progression. In: 7th International Conference on Automatic Face and Gesture Recognition (FGR06), pp. 341–345. IEEE (2006)

    Google Scholar 

  16. Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07-49, University of Massachusetts, Amherst (2007)

    Google Scholar 

  17. Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., Zhao, D.: The CAS-PEAL large-scale Chinese face database and baseline evaluations. IEEE Trans. Syst., Man, Cybern.-Part A: Syst. Hum. 38, 149–161 (2008)

    Article  Google Scholar 

  18. Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-PIE. Image Vis. Comput. 28, 807–813 (2010)

    Article  Google Scholar 

  19. Eidinger, E., Enbar, R., Hassner, T.: Age and gender estimation of unfiltered faces. IEEE Trans. Inf. Forensics Secur. 9, 2170–2179 (2014)

    Article  Google Scholar 

  20. Shan, C.: Learning local binary patterns for gender classification on real-world face images. Pattern Recognit. Lett. 33, 431–437 (2012)

    Article  Google Scholar 

  21. Shih, H.C.: Robust gender classification using a precise patch histogram. Pattern Recognit. 46, 519–528 (2013)

    Article  Google Scholar 

  22. Tapia, J.E., Perez, C.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 Secur. 8, 488–499 (2013)

    Article  Google Scholar 

  23. Bekios-Calfa, J., Buenaposada, J.M., Baumela, L.: Robust gender recognition by exploiting facial attributes dependencies. Pattern Recognit. Lett. 36, 228–234 (2014)

    Article  Google Scholar 

  24. Jia, S., Cristianini, N.: Learning to classify gender from four million images. Pattern Recognit. Lett. 58, 35–41 (2015)

    Article  Google Scholar 

  25. Antipov, G., Berrani, S.A., Dugelay, J.L.: Minimalistic CNN-based ensemble model for gender prediction from face images. Pattern Recognit. Lett. 70, 59–65 (2016)

    Article  Google Scholar 

  26. Golomb, B.A., Lawrence, D.T., Sejnowski, T.J.: SEXNET: a neural network identifies sex from human faces. In: NIPS, vol. 1, p. 2 (1990)

    Google Scholar 

  27. Gutta, S., Wechsler, H., Phillips, P.J.: Gender and ethnic classification of face images. In: 1998 Third IEEE International Conference on Automatic Face and Gesture Recognition, Proceedings, pp. 194–199. IEEE (1998)

    Google Scholar 

  28. Moghaddam, B., Yang, M.H.: Learning gender with support faces. IEEE Trans. Pattern Anal. Mach. Intell. 24, 707–711 (2002)

    Article  Google Scholar 

  29. Kim, H.C., Kim, D., Ghahramani, Z., Bang, S.Y.: Appearance-based gender classification with gaussian processes. Pattern Recognit. Lett. 27, 618–626 (2006)

    Article  Google Scholar 

  30. Baluja, S., Rowley, H.A.: Boosting sex identification performance. Int. J. Comput. Vis. 71, 111–119 (2007)

    Article  Google Scholar 

  31. Levi, G., Hassner, T.: Age and gender classification using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 34–42 (2015)

    Google Scholar 

  32. Jain, A., Huang, J.: Integrating independent components and linear discriminant analysis for gender classification. In: 2004 Sixth IEEE International Conference on Automatic Face and Gesture Recognition, Proceedings, pp. 159–163. IEEE (2004)

    Google Scholar 

  33. Khan, A., Majid, A., Mirza, A.M.: Combination and optimization of classifiers in gender classification using genetic programming. Int. J. Knowl.-Based Intell. Eng. Syst. 9, 1–11 (2005)

    Google Scholar 

  34. Bekios-Calfa, J., Buenaposada, J.M., Baumela, L.: Revisiting linear discriminant techniques in gender recognition. IEEE Trans. Pattern Anal. Mach. Intell. 33, 858–864 (2011)

    Article  Google Scholar 

  35. Tariq, U., Hu, Y., Huang, T.S.: Pattern recognition, machine intelligence and biometrics. In: Wang, S.P. (ed.) Gender and Race Identification by Man and Machine, 1st edn. Springer, Berlin, Heidelberg (2011). doi:10.1007/978-3-642-22407-2

    Google Scholar 

  36. Sun, N., Zheng, W., Sun, C., Zou, C., Zhao, L.: Gender classification based on boosting local binary pattern. In: Wang, J., Yi, Z., Zurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 194–201. Springer, Heidelberg (2006). doi:10.1007/11760023_29

    Chapter  Google Scholar 

  37. Xia, B., Sun, H., Lu, B.L.: Multi-view gender classification based on local Gabor binary mapping pattern and support vector machines. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 3388–3395. IEEE (2008)

    Google Scholar 

  38. Toews, M., Arbel, T.: Detection, localization, and sex classification of faces from arbitrary viewpoints and under occlusion. IEEE Trans. Pattern Anal. Mach. Intell. 31, 1567–1581 (2009)

    Article  Google Scholar 

  39. Wang, J.G., Li, J., Yau, W.Y., Sung, E.: Boosting dense sift descriptors and shape contexts of face images for gender recognition. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, pp. 96–102. IEEE (2010)

    Google Scholar 

  40. Guo, G., Dyer, C.R., Fu, Y., Huang, T.S.: Is gender recognition affected by age? In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 2032–2039. IEEE (2009)

    Google Scholar 

  41. Chen, C., Ross, A.: Evaluation of gender classification methods on thermal and near-infrared face images. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–8. IEEE (2011)

    Google Scholar 

  42. Reid, D., Samangooei, S., Chen, C., Nixon, M., Ross, A.: Soft biometrics for surveillance: an overview. In: Machine Learning: Theory and Applications, pp. 327–352. Elsevier (2013)

    Google Scholar 

  43. Sanderson, C., Lovell, B.C.: Multi-region probabilistic histograms for robust and scalable identity inference. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 199–208. Springer, Heidelberg (2009). doi:10.1007/978-3-642-01793-3_21

    Chapter  Google Scholar 

  44. http://conradsanderson.id.au/lfwcrop/

  45. Huang, G., Mattar, M., Lee, H., Learned-Miller, E.G.: Learning to align from scratch. In: Advances in Neural Information Processing Systems, pp. 764–772 (2012)

    Google Scholar 

  46. Bingham, E., Mannila, H.: Bingham, E., Mannila, H.: Random projection in dimensionality reduction: applications to image and text data. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 245–250. ACM (2001)

    Google Scholar 

  47. https://www.tensorflow.org/

Download references

Acknowledgements

This work was supported in part by a faculty research grant, FRG15-R-42. We thank the efforts of Mohamed, Riaz, Siyam and Zeid who helped in manual verification of gender labels.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, College of Engineering, American University of Sharjah, Sharjah, UAE

    Ahsan Jalal & Usman Tariq

Authors
  1. Ahsan Jalal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Usman Tariq
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahsan Jalal .

Editor information

Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University , Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Jalal, A., Tariq, U. (2017). The LFW-Gender Dataset. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10118. Springer, Cham. https://doi.org/10.1007/978-3-319-54526-4_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54526-4_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54525-7

  • Online ISBN: 978-3-319-54526-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation