자발적 웃음과 인위적 웃음 간의 구분: 사람 대 컴퓨터
Discrimination between spontaneous and posed smile: Humans versus computers
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본 연구에서는 자발적인 웃음과 인위적인 웃음을 변별하는 데 있어서 일반 사람들의 정확도와 컴퓨터를 이용한 분류 알고리즘의 정확도를 비교하였다. 실험참가자들은 단일 영상 판단 과제와 쌍비교 판단과제를 수행하였다. 단일 영상판단 과제는 웃음 영상을 한 장씩 제시하면서 이 영상의 웃음이 자발적인 것인지 인위적인 것인지를 판단하는 것이었으며, 쌍비교 판단과제는 동일한 사람에게서 얻은 두 종류의 웃음 영상을 동시에 제시하면서 자발적인 웃음 영상이 어떤 것인지 판단하는 것이었다. 분류 알고리즘의 정확도를 산출하기 위하여 웃음 영상 각각에서 8 종류의 얼굴 특성치들을 추출하였다. 약 50%의 영상을 사용하여 단계적 선형판별분석을 수행하였으며, 여기서 산출된 판별함수를 이용하여 나머지 영상을 분류하였다. 단일 영상에 대한 판단결과, 단계적 선형판별분석의 정확도가 사람들의 정확도보다 높았다. 쌍비교에 대한 판단결과도 단계적 선형판별분석의 정확도가 사람들의 정확도보다 높았다. 20명의 실험참가자 중 선형판별분석의 정확도를 넘어서는 사람은 없었다. 판별분석에 중요하게 사용된 얼굴 특성치는 눈머리의 각도로, 눈을 가늘게 뜬 정도를 나타낸다. Ekman의 FACS에 따르면, 이 특성치는 AU 6에 해당한다. 사람들의 정확도가 낮은 이유는 두 종류의 웃음을 구별할 때, 눈에 관한 정보를 충분히 사용하지 않았기 때문으로 추론되었다.
Abstract
The study compares accuracies between humans and computer algorithms in the discrimination of spontaneous smiles from posed smiles. For this purpose, subjects performed two tasks, one was judgment with single pictures and the other was judgment with pair comparison. At the task of judgment with single pictures, in which pictures of smiling facial expression were presented one by one, subjects were required to judge whether smiles in the pictures were spontaneous or posed. In the task for judgment with pair comparison, in which two kinds of smiles from one person were presented simultaneously, subjects were to select spontaneous smile. To calculate the discrimination algorithm accuracy, 8 kinds of facial features were used. To calculate the discriminant function, stepwise linear discriminant analysis (SLDA) was performed by using approximately 50 % of pictures, and the rest of pictures were classified by using the calculated discriminant function. In the task of single pictures, the accuracy rate of SLDA was higher than that of humans. In the analysis of accuracy on pair comparison, the accuracy rate of SLDA was also higher than that of humans. Among the 20 subjects, none of them showed the above accuracy rate of SLDA. The facial feature contributed to SLDA effectively was angle of inner eye corner, which was the degree of the openness of the eyes. According to Ekman’s FACS system, this feature corresponds to AU 6. The reason why the humans had low accuracy while classifying two kinds of smiles, it appears that they didn’t use the information coming from the eyes enough.
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