Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Perceived Age Prediction from Face Image Based on Super-resolution and Tanh-polar Transform

얼굴영상의 초해상도화 및 Tanh-polar 변환 기반의 인지나이 예측

  • Ilkoo Ahn (KM Data Division, Korea Institute of Oriental Medicine) ;
  • Siwoo Lee (KM Data Division, Korea Institute of Oriental Medicine)
  • 안일구 (한국한의학연구원 한의약데이터부) ;
  • 이시우 (한국한의학연구원 한의약데이터부)
  • Received : 2023.09.27
  • Accepted : 2023.10.16
  • Published : 2023.10.31
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Abstract

Perceived age is defined as age estimated based on physical appearance. Perceived age is an important indicator of the overall health status of the elderly. This is because people who appear older tend to have higher rates of morbidity and mortality than people of the same chronological age. Although perceived age is an important indicator, there is a lack of objective methods to quantify perceived age. In this paper, we construct a quantified perceived age model from face images using a convolutional neural network. The face images are enlarged to super-resolution and the skin, an important feature in perceived age, is made clear. Moreover, through Tanh-polar transformation, the central area of the face occupies a relatively larger area than the boundary area, helping the neural network better recognize facial skin features. The experimental results show mean absolute error (MAE) of 6.59, showing that the proposed model is superior to existing method.

Keywords

Acknowledgement

본 연구는 한국한의학연구원의 '빅데이터 기반 한의 예방 치료 원천기술개발(KSN1731121)'의 지원을 받아 수행되었음.

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