The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Facial Age Estimation Using Convolutional Neural Networks Based on Inception Modules

인셉션 모듈 기반 컨볼루션 신경망을 이용한 얼굴 연령 예측

  • Sukh-Erdene, Bolortuya (Interdisciplinary Graduate Program for BIT Medical Convergence, Kangwon National University) ;
  • Cho, Hyun-chong (Dept. of Electronic Engineering and Interdisciplinary Graduate Program for BIT Medical Convergence, Kangwon National University)
  • Received : 2018.07.18
  • Accepted : 2018.08.04
  • Published : 2018.09.01
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Abstract

Automatic age estimation has been used in many social network applications, practical commercial applications, and human-computer interaction visual-surveillance biometrics. However, it has rarely been explored. In this paper, we propose an automatic age estimation system, which includes face detection and convolutional deep learning based on an inception module. The latter is a 22-layer-deep network that serves as the particular category of the inception design. To evaluate the proposed approach, we use 4,000 images of eight different age groups from the Adience age dataset. k-fold cross-validation (k = 5) is applied. A comparison of the performance of the proposed work and recent related methods is presented. The results show that the proposed method significantly outperforms existing methods in terms of the exact accuracy and off-by-one accuracy. The off-by-one accuracy is when the result is off by one adjacent age label to the above or below. For the exact accuracy, the age label of "60+" is classified with the highest accuracy of 76%.

Keywords

References

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