BFER-Net: Babies Facial Expression Recognition Model Using ResNet12 Enabled Few-Shot Embedding Adaptation and Convolutional Block Attention Modules

The recognition of babies’ facial expressions is challenging due to the limited availability of annotated data and the complex nature of their emotions.To address this problem, this work introduces a novel dataset, FER-BYC (Facial Expression Recognition for Bangladeshi Young Children), comprising 1,425 annotated images of babies’ facial expressions across seven emotional categories: disgust, fear, anger, happiness, neutral, sadness, and surprise. This dataset fills an enormous void in the domain, as there exist limited prior studies on babies’ facial expression detection. We’ve proposed a fusion model named BFER-Net. Here, we have deployed the feature extraction process. A Convolutional Block Attention Module (CBAM) was integrated into the Modified ResNet12 architecture. It allows the model to focus on the most relevant facial features. The implementation of few-shot learning techniques, like FEAT (Few-shot Embedding Adaptation with Transformer), Modified ResNet12, and Prototypical Networks, are especially appropriate for the small dataset size. Additionally, the proposed approach has been evaluated on the FER-BYC dataset, exhibiting 94.06% validation accuracy, which is better than the performance of traditional methods, and gaining higher classification accuracy. This research not only introduces new dataset but also gives a robust technique for baby facial expression recognition.