2023/02/08 by Hossein Rajoli, Rajoli, Hossein, Fatemeh Lotfi +5
Computer Science · Neuroscience · Psychology · #Artificial Intelligence (cs.AI) #Brain Tumor Detection and Classification #Computational Complexity (cs.CC) #Computer Science and Game Theory (cs.GT) #Computer Vision and Pattern Recognition (cs.CV) #Emotion and Mood Recognition #FOS: Computer and information sciences #Face and Expression Recognition
paper · pdf · doi:10.48550/arxiv.2302.04108
openalex publication_date 2023/02/08 · openalex created_date 2023/02/11 · openalex updated_date 2026/07/28
Facial expressions convey massive information and play a crucial role in emotional expression. Deep neural network (DNN) accompanied by deep metric learning (DML) techniques boost the discriminative ability of the model in facial expression recognition (FER) applications. DNN, equipped with only classification loss functions such as Cross-Entropy cannot compact intra-class feature variation or separate inter-class feature distance as well as when it gets fortified by a DML supporting loss item. The triplet center loss (TCL) function is applied on all dimensions of the sample's embedding in the embedding space. In our work, we developed three strategies: fully-synthesized, semi-synthesized, and prediction-based negative sample selection strategies. To achieve better results, we introduce a selective attention module that provides a combination of pixel-wise and element-wise attention coefficients using high-semantic deep features of input samples. We evaluated the proposed method on the RAF-DB, a highly imbalanced dataset. The experimental results reveal significant improvements in comparison to the baseline for all three negative sample selection strategies.