Recognition of facial expression using spatial transformation network and convolutional neural network

Jieun Kim; Eung-Joo Lee; Deokwoo Lee

doi:10.1117/12.2634030

27 May 2022 Recognition of facial expression using spatial transformation network and convolutional neural network

Jieun Kim, Eung-Joo Lee, Deokwoo Lee

Proceedings Volume 12101, Pattern Recognition and Tracking XXXIII; 121010J (2022) https://doi.org/10.1117/12.2634030
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

With the development of artificial intelligence, the field of sentiment analysis can be used in various industries such as computer-human interaction technology, personal status monitoring, criminal investigation, and entertainment. In the field of sentiment analysis, various methods such as facial expression, voice, EEG signal, and text are being studied. Among these methods, facial expression recognition is one of the approaches being actively studied because it has the advantage of being relatively easy to collect learning data and easy to apply to real life compared to other methods. Recently, research on facial expression recognition using deep learning has been actively conducted, and it shows relatively high performance. The method using deep learning has advantage of being easy to apply to a variety of data, but there is a limitation in large deviation in accuracy depending on the effect of occlusion, pose, and illumination in extracting feature points. In addition, in the case of expression recognition, similar objects such as face always exist in the data, and only some specific regions such as eyes, nose, and mouth have necessary information for learning, and remaining regions such as background and hair is considered as insignificant part of the data. Therefore learning all the features in the data not only takes a long time to learn, but also uses computing resources inefficiently. Therefore, we propose a convolutional neural network algorithm combined with a spatial transformation network which helps facial expression recognition by focusing on a specific part of the face.

Conference Presentation

Citation Download Citation

Jieun Kim, Eung-Joo Lee, and Deokwoo Lee "Recognition of facial expression using spatial transformation network and convolutional neural network", Proc. SPIE 12101, Pattern Recognition and Tracking XXXIII, 121010J (27 May 2022); https://doi.org/10.1117/12.2634030

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