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The advancement of deep learning techniques has revolutionized the field of computer vision, and FER has benefited substantially from these developments. Deep learning approaches for facial expression recognition have demonstrated remarkable progress, primarily leveraging convolutional neural networks and their variants to learn hierarchical feature representations directly from raw facial.
However, these current deep learning approaches rely solely on raw facial images and standard augmentation but lack explicit incorporation of facial anatomical knowledge. This forces models to discover expression-discriminative regions from scratch rather than leveraging established facial behavior research. |
IEEE/ICACT20260151 Slide.03
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