Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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Landmark Selection Using CNN-Based Heat Map for Facial Age Prediction

안면 연령 예측을 위한 CNN기반의 히트 맵을 이용한 랜드마크 선정

  • Hong, Seok-Mi (Department of Liberal Arts, Sangji University) ;
  • Yoo, Hyun (Contents Convergence Software Research Institute, Kyonggi University)
  • 홍석미 (상지대학교 교양대학) ;
  • 유현 (경기대학교 콘텐츠융합소프트웨어연구소)
  • Received : 2021.05.03
  • Accepted : 2021.07.20
  • Published : 2021.07.28
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Abstract

The purpose of this study is to improve the performance of the artificial neural network system for facial image analysis through the image landmark selection technique. For landmark selection, a CNN-based multi-layer ResNet model for classification of facial image age is required. From the configured ResNet model, a heat map that detects the change of the output node according to the change of the input node is extracted. By combining a plurality of extracted heat maps, facial landmarks related to age classification prediction are created. The importance of each pixel location can be analyzed through facial landmarks. In addition, by removing the pixels with low weights, a significant amount of input data can be reduced.

본 연구의 목적은 이미지 랜드마크 선정 기법을 기반으로, 인공신경망 안면 영상분석 시스템의 성능을 향상하기 위한 내용이다. 랜드마크 선정을 위하여 안면 이미지 연령을 분류를 위한 CNN 기반의 다층 ResNet 모델의 구성이 필요하며, ResNet 모델에서 입력 노드의 변화에 따른 출력 노드의 변화를 감지하는 히트 맵을 추출한다. 추출된 다수의 히트 맵을 결합하여 연령 구분 예측과 관계된 안면 랜드마크를 구성한다. 이를 통하여, 안면 랜드마크를 통하여 픽셀의 위치별 중요도를 분석할 수 있으며, 가중치가 낮은 픽셀의 제거함으로서 상당량의 입력 데이터 감소가 가능해졌다. 이러한 기법은 인공신경망 시스템의 연산 성능 향상에 기여하게 된다.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No.: NRF-2020R1A6A1A03040583).

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