얼굴 랜드마크 거리 특징을 이용한 표정 분류에 대한 연구
Study for Classification of Facial Expression using Distance Features of Facial Landmarks
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초록
표정 인식은 다양한 분야에서 지속적인 연구의 주제로서 자리 잡아 왔다. 본 논문에서는 얼굴 이미지 랜드마크 간의 거리를 계산하여 추출된 특징을 사용해 각 랜드마크들의 관계를 분석하고 5가지의 표정을 분류한다. 다수의 관측자들에 의해 수행된 라벨링 작업을 기반으로 데이터와 라벨 신뢰도를 높였다. 또한 원본 데이터에서 얼굴을 인식하고 랜드마크 좌표를 추출해 특징으로 사용하였으며 유전 알고리즘을 이용해 상대적으로 분류에 더 도움이 되는 특징을 선택하였다. 본 논문에서 제안한 방법을 이용하여 표정 인식 분류를 수행하였으며 제안된 방법을 이용하였을 때가 CNN을 이용하여 분류를 수행하였을 때보다 성능이 향상됨을 볼 수 있었다.
Abstract
Facial expression recognition has long been established as a subject of continuous research in various fields. In thispaper, the relationship between each landmark is analyzed using the features obtained by calculating the distancebetween the facial landmarks in the image, and five facial expressions are classified. We increased data and labelreliability based on our labeling work with multiple observers. In addition, faces were recognized from the original dataand landmark coordinates were extracted and used as features. A genetic algorithm was used to select features that arerelatively more helpful for classification. We performed facial recognition classification and analysis with the methodproposed in this paper, which shows the validity and effectiveness of the proposed method.
참고문헌 (15)
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