1 publications with the keyword "convolutional neural networks"

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.