Hybrid Deep Learning Algorithm for Real-Time Emotion Detection from Facial Expressions

Effective and real-time facial expression detection is becoming increasingly crucial in affective computing, intelligent user interfaces, and mental health screening to comprehend human emotions. Lighting, face angle, and occlusion changes typically challenge standard emotion recognition algorithms in real-life, ever-changing scenarios. This paper introduces EmotionSenseNet, a novel Emotion Sensing Network with Spatiotemporal and Attention Fusion, a deep learning network that combines spatial, temporal, and attentional data, improving emotion identification and targeting these issues. The design uses a lightweight CNN based on MobileNetV2 to extract spatial information and an LSTM network to define facial motion temporal correlations. The approach is simplified with a focus module highlighting essential face traits and periods. Face landmark alignment preprocessing provides input normalization across face pictures. The suggested technique has minimal latency and 91.3% accuracy on benchmark datasets like FER-2013 and CK+, making it suited for real-time edge device applications. Despite practical constraints, EmotionSenseNet can identify emotions reliably and scalable. Therefore, future research will focus on establishing more extensive emotional classifications and energy-efficient embedded system deployment techniques.