ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning

2 May 2023  ·  Azmine Toushik Wasi, Karlo Šerbetar, Raima Islam, Taki Hasan Rafi, Dong-Kyu Chae ·

In this paper, we introduce a framework ARBEx, a novel attentive feature extraction framework driven by Vision Transformer with reliability balancing to cope against poor class distributions, bias, and uncertainty in the facial expression learning (FEL) task. We reinforce several data pre-processing and refinement methods along with a window-based cross-attention ViT to squeeze the best of the data. We also employ learnable anchor points in the embedding space with label distributions and multi-head self-attention mechanism to optimize performance against weak predictions with reliability balancing, which is a strategy that leverages anchor points, attention scores, and confidence values to enhance the resilience of label predictions. To ensure correct label classification and improve the models' discriminative power, we introduce anchor loss, which encourages large margins between anchor points. Additionally, the multi-head self-attention mechanism, which is also trainable, plays an integral role in identifying accurate labels. This approach provides critical elements for improving the reliability of predictions and has a substantial positive effect on final prediction capabilities. Our adaptive model can be integrated with any deep neural network to forestall challenges in various recognition tasks. Our strategy outperforms current state-of-the-art methodologies, according to extensive experiments conducted in a variety of contexts.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Facial Expression Recognition (FER) AffWild2 ARBEx Accuracy 72.48 # 1
Facial Expression Recognition (FER) FER+ ARBEx Accuracy 93.09 # 2
Facial Expression Recognition (FER) FERG ARBEx Accuracy 98.18 # 2
Facial Expression Recognition (FER) JAFFE ARBEx Accuracy 96.67 # 1
Facial Expression Recognition (FER) RAF-DB ARBEx Overall Accuracy 92.47 # 3

Methods