文章基本信息

标题：Recognizing Human Emotions from Eyes and Surrounding Features: A Deep Learning Approach
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作者：Nymur Rahman Shuvo ; Ashiqul Islam ; Muhammad Shamsojjaman 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2021
卷号：12
期号：3
页码：386-394
DOI：10.14569/IJACSA.2021.0120346
出版社：Science and Information Society (SAI)
摘要：The need for an efficient intelligent system to detect human emotions is imperative. In this study, we proposed an automated convolutional neural network-based approach to recognize the human mental state from eyes and their surrounding features. We have applied deep convolutional neural network based Keras applications with the help of transfer learning and fine-tuning. We have worked with six universal emotions (i.e., happiness, disgust, sadness, fear, anger, and surprise) with a dataset containing 588 unique double eye images. In this study, we considered the eyes and their surrounding areas (Upper and lower eyelid, glabella, and brow) to detect the emotional state. The state and movement of the iris and pupil can vary with the various mental states. The common features found within the entire eyes during different mental states can help to capture human expression. The dataset was trained with pre-trained weights and used a confusion matrix to analyze the prediction to achieve better accuracy. The highest accuracy was achieved by DenseNet-201 is 91.78%, whereas VGG-16 and Inception-ResNet-v2 show 90.43% and 89.67%, respectively. This study will provide an insight into the current state of research to obtain better facial recognition.
关键词：Human emotion recognition; convolutional neural network (CNN); transfer learning; fine-tuning; VGG-16; Inception-ResNet -V2; DenseNet-201