ResNet50 and GRU: A Synergistic Model for Accurate Facial Emotion Recognition

Humans use voice, gestures, and emotions to communicate with one another. It improves oral communication effectiveness and facilitates concept of understanding. Majority of people are able to identify facial emotions with ease, regardless of gender, nationality, culture, or ethnicity. The recognition of facial expressions is becoming more and more significant in a variety of newly developed computing applications. Facial expression detection is a hot topic in almost every industry, including marketing, artificial intelligence, gaming, and healthcare. This study proposes a novel hybrid model combining ResNet-50 and Gated Recurrent Unit (GRU) for enhanced Facial emotion recognition (FER) accuracy. The dataset for the study is taken from Kaggle repository. ResNet-50, a deep convolutional neural network, excels in feature extraction by capturing intricate spatial hierarchies in facial images. GRU, effectively processes sequential data, capturing temporal dependencies crucial for emotion recognition. The integration of ResNet-50 and GRU leverages the strengths of both architectures, enabling robust and accurate emotion detection. Experimental result on CK+ dataset demonstrate that the proposed hybrid model outperforms current methods, achieving a remarkable accuracy of 95.56%. This superior performance underscores the model's potential for real-world applications in diverse domains such as security, healthcare, and interactive systems.