딥러닝기반 감정인식에서 데이터 불균형이 미치는 영향 분석
Effect Analysis of Data Imbalance for Emotion Recognition Based on Deep Learning
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초록
최근 들어 영유아를 대상으로 한 비대면 상담이 증가함에 따라 감정인식 보조 도구로 CNN기반 딥러닝 모델을 많이 사용하고 있다. 하지만대부분의 감정인식 모델은 성인 데이터 위주로 학습되어 있어 영유아 및 청소년을 대상으로 적용하기에는 성능상의 제약이 있다. 본 논문에서는이러한 성능제약의 원인을 분석하기 위하여 XAI 기법 중 하나인 LIME 기법을 통해 성인 대비 영유아와 청소년의 감정인식을 위한 얼굴 표정의특징을 분석한다. 뿐만 아니라 남녀 집단에도 동일한 실험을 수행함으로써 성별 간 얼굴 표정의 특징을 분석한다. 그 결과로 연령대별 실험 결과와성별별 실험 결과를 CNN 모델의 사전 훈련 데이터셋의 데이터 분포를 바탕으로 설명하고 균형 있는 학습 데이터의 중요성을 강조한다.
Abstract
In recent years, as online counseling for infants and adolescents has increased, CNN-based deep learning models are widely usedas assistance tools for emotion recognition. However, since most emotion recognition models are trained on mainly adult data, thereare performance restrictions to apply the model to infants and adolescents. In this paper, in order to analyze the performance constraints,the characteristics of facial expressions for emotional recognition of infants and adolescents compared to adults are analyzed throughLIME method, one of the XAI techniques. In addition, the experiments are performed on the male and female groups to analyze thecharacteristics of gender-specific facial expressions. As a result, we describe age-specific and gender-specific experimental results basedon the data distribution of the pre-training dataset of CNN models and highlight the importance of balanced learning data.
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