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Facial Expression Recognition Based on Deep Evolutional Spatial-Temporal Networks.

Kaihao Zhang, Yongzhen Huang, Yong Du

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 4, 2017
    PubMed
    Summary
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    This study introduces a novel deep evolutional spatial-temporal network for facial expression recognition. The proposed method effectively captures dynamic facial variations, significantly outperforming existing state-of-the-art approaches.

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Facial expression recognition (FER) faces challenges in capturing dynamic facial structure variations from videos.
    • Existing methods often struggle with integrating temporal and spatial information effectively.

    Purpose of the Study:

    • To propose a novel deep evolutional spatial-temporal network for enhanced facial expression recognition.
    • To effectively model both dynamic temporal features and static spatial features of facial expressions.

    Main Methods:

    • A part-based hierarchical bidirectional recurrent neural network (PHRNN) was developed to extract temporal features from facial landmarks.
    • A multi-signal convolutional neural network (MSCNN) was proposed to extract spatial features from still frames.

    Related Experiment Videos

  • A deep evolutional spatial-temporal network combining PHRNN and MSCNN was utilized, incorporating recognition and verification signals for training.

    • Main Results:

    • The proposed network effectively integrates partial-whole, geometry-appearance, and dynamic-still information.
    • Significant performance improvements were observed on benchmark datasets (CK+, Oulu-CASIA, MMI).
    • Error rates were reduced by 45.5%, 25.8%, and 24.4% on CK+, Oulu-CASIA, and MMI, respectively, compared to prior state-of-the-art methods.

    Conclusions:

    • The deep evolutional spatial-temporal network offers a robust solution for facial expression recognition.
    • The method demonstrates superior performance in capturing dynamic facial variations and expression evolution.
    • This approach represents a significant advancement in the field of automated facial expression analysis.