방송공학회논문지 (Journal of Broadcast Engineering)

  • 제24권4호
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  • Pages.564-572
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  • 2019
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  • 1226-7953(pISSN)
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  • 2287-9137(eISSN)
한국방송∙미디어공학회 (The Korean Institute of Broadcast and Media Engineers)
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얼굴 특징점 검출을 위한 적분 회귀 네트워크

Integral Regression Network for Facial Landmark Detection

  • Kim, Do Yeop (Department of Electronics and Communications Engineering, Kwangwoon University) ;
  • Chang, Ju Yong (Department of Electronics and Communications Engineering, Kwangwoon University)
  • 투고 : 2019.05.07
  • 심사 : 2019.07.05
  • 발행 : 2019.07.30
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초록

최근 딥러닝 기술의 발전과 함께 얼굴 특징점 검출 방법의 성능은 크게 향상되었다. 대표적인 얼굴 특징점 검출 방법인 히트맵 회귀 방법은 효율적이고 강력한 방법으로 널리 사용되고 있으나, 단일 네트워크를 통해 특징점 좌표를 즉시 얻을 수 없으며, 히트맵으로부터 특징점 좌표를 결정하는 과정에서 정확도가 손실된다는 단점이 존재한다. 이러한 문제점들을 해결하기 위해 본 논문에서는 기존의 히트맵 회귀 방법에 적분 회귀 방법을 결합할 것을 제안한다. 여러 가지 데이터셋을 사용한 실험을 통해 제안하는 적분 회귀 네트워크가 얼굴 특징점 검출 성능을 크게 향상시킨다는 것을 보인다.

With the development of deep learning, the performance of facial landmark detection methods has been greatly improved. The heat map regression method, which is a representative facial landmark detection method, is widely used as an efficient and robust method. However, the landmark coordinates cannot be directly obtained through a single network, and the accuracy is reduced in determining the landmark coordinates from the heat map. To solve these problems, we propose to combine integral regression with the existing heat map regression method. Through experiments using various datasets, we show that the proposed integral regression network significantly improves the performance of facial landmark detection.

키워드

BSGHC3_2019_v24n4_564_f0001.png 이미지

그림 1. 제안하는 얼굴 특징점 검출 네트워크의 개요 Fig. 1. Overview of proposed facial landmark detection network

BSGHC3_2019_v24n4_564_f0002.png 이미지

그림 2. 제안하는 방법(ResNet50-Int)과 비교 모델들의 NME 곡선 Fig. 2. NME curves of proposed method (ResNet50-Int) and baseline models

BSGHC3_2019_v24n4_564_f0003.png 이미지

그림 3. 제안하는 모델의 얼굴 특징점 검출 결과 (a) 성공적으로 검출한 경우 (b) 검출에 실패한 경우 Fig. 3. Examples of facial landmarks detected by proposed method (a) success cases (b) failure cases

표 1. 제안하는 네트워크의 세부 구성 Table 1. Detail of the proposed network

BSGHC3_2019_v24n4_564_t0001.png 이미지

표 2. 각 모델에 대한 AUC(%) Table 2. AUC (%) of each model

BSGHC3_2019_v24n4_564_t0002.png 이미지

표 3. 각 모델의 프레임 당 처리 시간 (ms) Table 3. Processing time per frame (ms) of each model

BSGHC3_2019_v24n4_564_t0003.png 이미지

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