2022/09/25 by Dae Ha Kim, Kim, Daeha, Byung Cheol Song +1 · 4 citations
Computer Science · Engineering · Neuroscience · #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Ferroelectric and Negative Capacitance Devices
paper · pdf · doi:10.48550/arxiv.2209.12172
openalex publication_date 2022/09/25 · openalex created_date 2022/09/28 · openalex updated_date 2026/07/28
Identity-invariant facial expression recognition (FER) has been one of the challenging computer vision tasks. Since conventional FER schemes do not explicitly address the inter-identity variation of facial expressions, their neural network models still operate depending on facial identity. This paper proposes to quantify the inter-identity variation by utilizing pairs of similar expressions explored through a specific matching process. We formulate the identity matching process as an Optimal Transport (OT) problem. Specifically, to find pairs of similar expressions from different identities, we define the inter-feature similarity as a transportation cost. Then, optimal identity matching to find the optimal flow with minimum transportation cost is performed by Sinkhorn-Knopp iteration. The proposed matching method is not only easy to plug in to other models, but also requires only acceptable computational overhead. Extensive simulations prove that the proposed FER method improves the PCC/CCC performance by up to 10% or more compared to the runner-up on wild datasets. The source code and software demo are available at https://github.com/kdhht2334/ELIMFER.