Keywords
Multi-Task Learning (MTL)
Lightweight Neural Networks (LNN)
How to Cite
Abstract
This article delves into the advancements of emotion recognition technologies, emphasizing the synergy between multi-task learning strategies and lightweight neural networks. With the rapid progression in facial expression and attributes recognition, the integration of these methodologies offers promising solutions to challenges like computational efficiency and robust performance. Through systematic data preprocessing, optimized neural network architecture, rigorous model training, and comprehensive evaluation, the proposed model exhibits superior performance compared to traditional baseline models. The study underscores the potential of this integrative approach, suggesting a future trajectory for sustainable and efficient facial recognition technologies.
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