The Journal of the Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회논문지)

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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High Efficiency Adaptive Facial Expression Recognition based on Incremental Active Semi-Supervised Learning

점진적 능동준지도 학습 기반 고효율 적응적 얼굴 표정 인식

  • 김진우 (인하대학교 컴퓨터공학부) ;
  • 이필규 (인하대학교 컴퓨터공학부)
  • Received : 2017.02.21
  • Accepted : 2017.04.07
  • Published : 2017.04.30
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Abstract

It is difficult to recognize Human's facial expression in the real-world. For these reason, when database and test data have similar condition, we can accomplish high accuracy. Solving these problem, we need to many facial expression data. In this paper, we propose the algorithm for gathering many facial expression data within various environment and gaining high accuracy quickly. This algorithm is training initial model with the ASSL (Active Semi-Supervised Learning) using deep learning network, thereafter gathering unlabeled facial expression data and repeating this process. Through using the ASSL, we gain proper data and high accuracy with less labor force.

사람의 얼굴 표정을 실제 환경에서 인식하는 데에는 여러 가지 난이한 점이 존재한다. 그래서 학습에 사용된 데이터베이스와 실험 데이터가 여러 가지 조건이 비슷할 때에만 그 성능이 높게 나온다. 이러한 문제점을 해결하려면 수많은 얼굴 표정 데이터가 필요하다. 본 논문에서는 능동준지도 학습을 통해 다양한 조건의 얼굴 표정 데이터를 쉽게 모으고 보다 빠르게 성능을 확보할 수 있는 방법을 제안한다. 제안하는 알고리즘은 딥러닝 네트워크와 능동 학습 (Active Learning)을 통해 초기 모델을 학습하고, 이후로는 준지도 학습(Semi-Supervised Learning)을 통해 라벨이 없는 추가 데이터를 확보하며, 성능이 확보될 때까지 이러한 과정을 반복한다. 위와 같은 능동준지도 학습(Active Semi-Supervised Learning)을 통해서 보다 적은 노동력으로 다양한 환경에 적합한 데이터를 확보하여 성능을 확보할 수 있다.

Keywords

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