Real-Time Facial Attribute Recognition Using Multi-Task Learning

In the realm of facial attribute recognition, crucial for applications like video surveillance, face retrieval, and recommendation systems, existing approaches often fall
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hort in realistic scenarios, particularly for low-cost embedded systems. In this paper, we propose a Deep Multi-Task Learning approach to concurrently estimate multiple facial attributes from a single face image. We use convolutional neural networks to learn the commonalities and dissimilarities among various attributes. To address ordinal attribute estimation, we transform the original regression problem into a linear combination of binary classification subproblems, effectively reducing estimation errors. Experimental results from diverse datasets underscore the superior performance of our proposed approach. Finally, we present a practical solution for the cost-effective and swift application of our approach in realistic scenarios.