Browse > Article

Estimation of gender and age using CNN-based face recognition algorithm  

Lim, Sooyeon (School of Game, Dongyang University)
Publication Information
International journal of advanced smart convergence / v.9, no.2, 2020 , pp. 203-211 More about this Journal
This study proposes a method for estimating gender and age that is robust to various external environment changes by applying deep learning-based learning. To improve the accuracy of the proposed algorithm, an improved CNN network structure and learning method are described, and the performance of the algorithm is also evaluated. In this study, in order to improve the learning method based on CNN composed of 6 layers of hidden layers, a network using GoogLeNet's inception module was constructed. As a result of the experiment, the age estimation accuracy of 5,328 images for the performance test of the age estimation method is about 85%, and the gender estimation accuracy is about 98%. It is expected that real-time age recognition will be possible beyond feature extraction of face images if studies on the construction of a larger data set, pre-processing methods, and various network structures and activation functions have been made to classify the age classes that are further subdivided according to age.
gender estimation; age estimation; face recognition; CNN;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Y. H. Kwon and D. Vitoria Lobo, "Age classification from facial images," Computer vision and image understanding, Vol. 74, pp. 1-21, 1999. DOI:   DOI
2 J. K. Pontes, A. S. Britto Jr, C. Fookes, and A. L. Koerich, "A flexible hierarchical approach for facial age estimation based on multiple features," Pattern Recognition, Vol. 54, pp. 34-51, 2016. DOI:   DOI
3 A. Gunay and V. V. Nabiyev, "Automatic age classification with LBP," in Proc. 23rd International Symposium on Computer and Information Sciences, 2008. DOI:
4 C. Shan, "Learning local binary patterns for gender classification on real-world face image," Pattern Recognition Letters, Vol. 33, no. 4, pp. 431-437, 2012. DOI:   DOI
5 J. Ylioinas, A. Hadid, X. Hong and M. Pietikainen, "Age Estimation Using Local Binary Pattern Kernel Density Estimate," in Proc. International Conference on Image Analysis and Processing, pp. 141-150, 2013. DOI:
6 D. Nguyen, S. Cho, and K. Park, "Human Age Estimation Based on Multi-level Local Binary Pattern and Regression Method," Future Information Technology, Vol. 309, pp. 433-438, 2014. DOI:   DOI
7 R. Rothe, R. Timofte and L. V. Gool, "DEX: Deep EXpectation of apparent age from a single image," in Proc. IEEE International Conference on Computer Vision Workshops, Dec, 2015. DOI:
8 C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich, "Going Deeper with Convolutions," in Proc. IEEE Conference Computer Vision and Pattern Recognition, 2015. DOI:
9 T. R. Kalansuriya, and A. T. Dharmaratne, "Neural Network based Age and Gender Classification for Facial Images," International Journal on Advances in ICT for Emerging Regions, vol. 7, no. 2, 2014. DOI:
10 Y. Taigman et al., "DeepFace: Closing the Gap to Human-Level Performance in Face Verification," in Proc. IEEE Conference Computer Vision and Pattern Recognition, pp. 1701-1708, 2014. DOI:
11 O. M. Parkhi, A. Vedaldi and A. Zisserman, "Deep face recognition," in Proc. British Machine Vision Vol. 1, No 3, pp. 6-17, 2015. DOI:
12 F. Schroff, D. Kalenichenko and J. Philbin, "FaceNet: A Unified Embedding for Face Recognition and Clustering," in Proc. IEEE Conference Computer Vision and Pattern Recognition, pp. 815-823, 2015. DOI:
13 N. Dalal and B. Triggs, "Histogram of oriented gradients for human detection," in Proc. IEEE Conference Computer Vision and Pattern Recognition, pp. 886-893, 2005. DOI:
14 A. Krizhevsky, I. Sutskever and G. Hinton, "ImageNet classification with deep convolutional neural networks," in Proc. Neural Information Processing Systems Conference, 2012. DOI:
15 G. Huang et al., "Labeled Faces in the wild: A Database for Studying Face Recognition in Unconstrained Environments," Univ. of Massachusetts, Amherst, Technical Report 07-49, 2007.
16 L. Wolf, T Hassner, and I. Maoz, "Face Recognition in Unconstrained Videos with Matched Background Similarity," in Proc. IEEE Conference Computer Vision and Pattern Recognition, pp. 529-534, 2011. DOI:
17 B. F. Klare et al., "Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A," in Proc. IEEE Conference Computer Vision and Pattern Recognition, pp. 1931-1939, 2015. DOI:
18 I. Kemelmacher-Shlizerman et al., "The MegaFace Benchmark: 1 Million Faces for Recognition at Scale," in Proc. IEEE Conference Computer Vision and Pattern Recognition, pp. 4873-4882, 2016. DOI:
19 IMDB-WIKI-500k+ face images with age and gender labels.\