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Intelligent Deep Neural Framework for Instant Face Matching and Gender Detection
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Keywords
Deep learning
face recognition
gender prediction
convolutional neural network
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Abstract
Face recognition and gender prediction remain pivotal tasks in computer vision, underpinning applications from security systems to human-computer interaction. This paper proposes an intelligent deep neural framework for real-time face matching and gender detection, leveraging multi-layer convolutional architectures and optimized feature representations. By integrating structural binary gradient patterns, multi-texture local ternary descriptors, and hypersphere embedding strategies, the framework achieves robust identification under varying illumination, occlusion, and expression conditions. Experimental findings demonstrate enhanced accuracy in both face verification and gender classification, validating the efficacy of the proposed approach. The study also critically examines limitations, including computational overhead and dataset biases, providing a roadmap for future research in scalable, adaptive facial analysis systems (J. Chen et al., 2017; W. Huang and H. Yin, 2017; M. Wang and W. Deng, 2020).
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