#192:
G. Yolcu,
I. Oztel,
S. Kazan,
C. Oz,
K. Palaniappan,
T. Lever, and
F. Bunyak
EEE International Conference on Bioinformatics and Biomedicine (BIBM) ,
pgs. 1652-1657,
2017
Abstract,
Bibtex,
PlainText,
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Facial expressions play an important role in com- munication. Impaired facial expression is a common sign of numerous medical conditions, particularly neurological disor- ders. Accurate automated systems are needed to recognize facial expressions and to reveal valuable information that can be used for diagnosis and monitoring of neurological disorders. This paper presents a novel deep learning approach for automatic facial expression recognition. The proposed architecture first segments the facial components known to be important for facial expression recognition and forms an iconized image; then performs facial expression classification using the obtained iconized facial components image combined with the raw facial images. This approach integrates local part-based features with holistic facial information for robust facial expression recognition. Preliminary experimental results using the proposed system achieved 93.43% facial expression recognition accuracy, more than 6% accuracy improvement compared to facial expression recognition from raw input images. The goal of the proposed study is design of a noninvasive, objective, and quantitative facial expression recognition system to assist diagnosis and monitoring of neurological disorders affecting facial expressions.
@inproceedings{2018a,
author = "G. Yolcu and I. Oztel and S. Kazan and C. Oz and K. Palaniappan and T. Lever and F. Bunyak",
title = "Deep learning-based facial expression recognition for monitoring neurological disorders",
year = 2017,
journal = "EEE International Conference on Bioinformatics and Biomedicine (BIBM) ",
pages = "1652-1657",
month = "Nov",
keywords = "convolutional neural networks, deep learning, facial component segmentation, facial expression recognition",
url = "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8217907"
}
G. Yolcu, I. Oztel, S. Kazan, C. Oz, K. Palaniappan, T. Lever, and F. Bunyak. Deep learning-based facial expression recognition for monitoring neurological disorders. EEE International Conference on Bioinformatics and Biomedicine (BIBM) , pages 1652-1657, November 2017.