영유아 이상징후 감지를 위한표정 인식 알고리즘 개선
The improved facial expression recognition algorithm for detecting abnormal symptoms in infants and young children
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초록
비접촉형 체온 측정 시스템은 광학 및 열화상 카메라를 활용하여 집단시설의 발열성 질병을 관리하는 핵심 요소 중 하나이다. 기존 체온 측정 시스템은 딥러닝 기반 얼굴검출 알고리즘이 사용되어 얼굴영역의 단순 체온 측정에는 활용할 수 있지만, 의사표현이 어려운 영유아의 이상 징후를 인지하는데 한계가 있다. 본 논문에서는 기존의 체온 측정 시스템에서 영유아의 이상징후 감지를 위해 표정인식 알고리즘을 개선한다. 제안된 방법은 객체탐지 모델을 사용하여 영상에서 영유아를 검출한 후 얼굴영역을 추출하고 표정인식의 핵심 요소인 눈, 코, 입의 좌표를 획득한다. 이후 획득된 좌표를 기반으로 선택적 샤프닝 필터를 적용하여 표정인식을 진행한다. 실험결과에 따르면 제안된 알고리즘은 UTK 데이터셋에서 무표정, 웃음, 슬픔3가지 표정에 대해 각각 2.52%, 1.12%, 2.29%가 향상되었다.
Abstract
The non-contact body temperature measurement system is one of the key factors, which is manage febrile diseases inmass facilities using optical and thermal imaging cameras. Conventional systems can only be used for simple bodytemperature measurement in the face area, because it is used only a deep learning-based face detection algorithm. So,there is a limit to detecting abnormal symptoms of the infants and young children, who have difficulty expressing theiropinions. This paper proposes an improved facial expression recognition algorithm for detecting abnormal symptoms ininfants and young children. The proposed method uses an object detection model to detect infants and young children inan image, then It acquires the coordinates of the eyes, nose, and mouth, which are key elements of facial expressionrecognition. Finally, facial expression recognition is performed by applying a selective sharpening filter based on theobtained coordinates. According to the experimental results, the proposed algorithm improved by 2.52%, 1.12%, and2.29%, respectively, for the three expressions of neutral, happy, and sad in the UTK dataset.
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