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Understanding Human Face Shape via Inception-ResNet Neural Network Architecture | IEEE Conference Publication | IEEE Xplore

Understanding Human Face Shape via Inception-ResNet Neural Network Architecture


Abstract:

In recent years, computer vision has advanced facial recognition applications across diverse domains, from security systems to augmented reality. Among the fundamental at...Show More

Abstract:

In recent years, computer vision has advanced facial recognition applications across diverse domains, from security systems to augmented reality. Among the fundamental attributes of a face, shape plays a crucial role in distinguishing individuals and understanding their unique identities. This paper introduces an innovative approach to comprehending human face shape through the cutting-edge Inception-ResNet neural network architecture, which combines Inception with residual connections. The approach harnesses the rich discriminative features learned by this architecture to boost face shape recognition accuracy and robustness. Extensive experiments demonstrate the remarkable performance of the approach, surpassing traditional methods and prior deep learning models. Furthermore, to provide a holistic perspective on the individual contributions of various modules within our approach, we present a detailed ablation study.
Date of Conference: 14-15 November 2023
Date Added to IEEE Xplore: 13 February 2024
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Conference Location: Surabaya, Indonesia

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