ENHANCING FACIAL EXPRESSION AND ATTRIBUTES
RECOGNITION: AN EXPLORATION OF MULTI-TASK
LEARNING WITHIN LIGHTWEIGHT NEURAL NETWORKS
26.11.2023
International Scientific Journal "Science and Innovation". Series A. Volume 2 Issue 11
Abstract. Facial recognition, especially in the domains of expression and attribute
detection, has become pivotal in numerous applications. This study delves into the synergistic
integration of multi-task learning techniques with lightweight neural networks to address the dual
challenges of computational efficiency and robust performance. The research findings underscore
the potential of this combined approach, revealing a significant improvement in the recognition of
facial expressions and attributes. Furthermore, the proposed framework exhibits enhanced
efficiency, making it ideal for real-world applications that demand rapid and accurate facial
analysis.
Keywords: facial recognition, multi-task learning, Lightweight neural networks,
computational efficiency, robust performance, facial attributes, expression detection, real-world
applications.
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