The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
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Age and Gender Classification with Small Scale CNN

소규모 합성곱 신경망을 사용한 연령 및 성별 분류

  • ;
  • 류재흥 (전남대학교 컴퓨터공학과)
  • Received : 2021.12.31
  • Accepted : 2022.02.17
  • Published : 2022.02.28
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Abstract

Artificial intelligence is getting a crucial part of our lives with its incredible benefits. Machines outperform humans in recognizing objects in images, particularly in classifying people into correct age and gender groups. In this respect, age and gender classification has been one of the hot topics among computer vision researchers in recent decades. Deployment of deep Convolutional Neural Network(: CNN) models achieved state-of-the-art performance. However, the most of CNN based architectures are very complex with several dozens of training parameters so they require much computation time and resources. For this reason, we propose a new CNN-based classification algorithm with significantly fewer training parameters and training time compared to the existing methods. Despite its less complexity, our model shows better accuracy of age and gender classification on the UTKFace dataset.

인공지능은 놀라운 이점으로 우리 삶의 중요한 부분을 차지하고 있다. 기계는 이미지에서 물체를 인식하는 것, 특히 사람들을 정확한 나이와 성별 그룹으로 분류하는 것에 있어서 인간을 능가하고 있다. 이러한 측면에서 나이와 성별 분류는 최근 수십 년 동안 컴퓨터 비전 연구자들 사이에서 뜨거운 주제 중 하나였다. 심층 합성곱 신경망(CNN) 모델의 배포는 최첨단 성능을 달성했다. 그러나 대부분의 CNN 기반 아키텍처는 수십 개의 훈련 매개 변수로 매우 복잡하기 때문에 많은 계산 시간과 자원이 필요하다. 이러한 이유로 기존 방법에 비해 훈련 매개 변수와 훈련 시간이 현저히 적은 새로운 CNN기반 분류 알고리즘을 제안한다. 덜 복잡함에도 불구하고 우리 모델은 UTKFace 데이터 세트에서 연령 및 성별 분류의 더 나은 정확도를 보여준다.

Keywords

References

  1. G. Levi and T. Hassner, "Age and gender classification using convolutional neural networks," IEEE Conf. on Computer Vision and Pattern Recognition Workshops, 2015, pp. 34-42.
  2. O. Agbo-Ajala and S. Viriri, "Deeply Learned Classifiers for Age and Gender Predictions of Unfiltered Faces," Hindawi The Scientific World Journal, vol. 2020, AID 1289408, Apr. 2020, pp. 1-12.
  3. Y. Kwon and N. da V. Lobo, "Age classification from facial images," Computer vision and image understanding, vol. 74, 1999, pp. 1-21. https://doi.org/10.1006/cviu.1997.0549
  4. Z. Zhang, Y. Song, and H. Qi, "Age Progression/Regression by Conditional Adversarial Autoencoder," IEEE Conf. on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 2017, pp. 4352-4360.
  5. Y. Lin, J. Shen, Y. Wang, and M. Pantic, "FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild," arXiv preprint arXiv:2106.11145, 2021, pp. 1-10.
  6. D. Yi, Z. Lei, and S. Z. Li, "Age estimation by multi-scale convolutional network," in Computer Vision - ACCV 2014, Workshops: Singapore, Singapore, Nov. 2014, pp. 144-158.
  7. E. Eidinger, R. Enbar, and T. Hassner, "Age and gender estimation of unfiltered faces," IEEE Trans. on Information Forensics and Security, vol. 9, no. 12, 2014, pp. 2170-2179. https://doi.org/10.1109/TIFS.2014.2359646
  8. S. Chen, C. Zhang, M. Dong, J. Le, and M. Rao, "Using Ranking-CNN for Age Estimation," in 2017 IEEE Conf. on Computer Vision and Pattern Recognition, July 2017, pp. 742-751
  9. R. Rothe, R. Timofte, and L. V. Gool, "DEX: Deep Expectation of Apparent Age from a Single Image," in IEEE Int. Conf. on Computer Vision Workshop, Dec. 2015, pp. 252-257.
  10. R. Rothe, R. Timofte, and L. V. Gool, "Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks," Int. J. of Computer Vision, vol. 126, no. 2, Apr. 2018, pp. 144-157. https://doi.org/10.1007/s11263-016-0940-3
  11. B. B. Gao, H. Y. Zhou, J. Wu, and X. Geng, "Age estimation using expectation of label distribution learning," in Proc. of the 27th Int. Joint Conf. on Artificial Intelligence, Stockholm, Sweden, July 2018, pp. 712-718.
  12. D. P. Kingma and L. J. Ba, "Adam, A method for stochastic optimization," In Proceedings of the Int. Conf. for Learning Representations (ICLR), San Diego, CA, USA, May 2015, pp. 1-15.
  13. S. Salimov and J. Yoo, "A Design of Small Scale Deep CNN Model for Facial Expression Recognition using the Low Resolution Image Datasets," J. of the Korea Institute of Electronic Communication Sciences, vol. 16, no. 1, 2021, pp. 75-80. https://doi.org/10.13067/JKIECS.2021.16.1.75
  14. J. Yoo, "An Extension of Unified Bayesian Tikhonov Regularization Method and Application to Image Restoration," J. of the Korea Institute of Electronic Communication Sciences, vol. 15, no. 01, 2020, pp. 161-166.
  15. J. Yoo, "A Unified Bayesian Tikhonov Regularization Method for Image Restoration," J. of the Korea Institute of Electronic Communication Sciences, vol. 11, no. 11, 2016, pp. 1129-1134. https://doi.org/10.13067/JKIECS.2016.11.11.1129
  16. J. Yoo, "Self-Regularization Method for Image Restoration," J. of the Korea Institute of Electronic Communication Sciences, vol. 11, no. 1, 2016, pp. 45-52. https://doi.org/10.13067/JKIECS.2016.11.1.45
  17. S. Park, "Optimal QP Determination Method for Adaptive Intra Frame Encoding," J. of the Korea Institute of Electronic Communication Sciences, vol. 10, no. 9, 2015, pp. 1009-1018. https://doi.org/10.13067/JKIECS.2015.10.9.1009