Abstract
Skin identification can be used in several security applications such as border’s security checkpoints and facial recognition in bio-metric systems. Traditional skin identification techniques were unable to deal with the high complexity and uncertainty of human skin in uncontrolled environments. To address this gap, this research proposes a new skin identification technique using deep convolutional neural network. The proposed sequential deep model consists of three blocks of convolutional layers, followed by a series of fully connected layers, optimized to maximize skin texture classification accuracy. The proposed model performance has been compared with some of the well-known texture-based skin identification techniques and delivered superior results in terms of overall accuracy. The experiments were carried out over two datasets including FSD Benchmark dataset as well as an in-house skin texture patch dataset. Results show that the proposed deep skin identification model with highest reported accuracy of 0.932 and minimum loss of 0.224 delivers reliable and robust skin identification.
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Acknowledgments
This research is supported by the MIDAS project (agreement G5381), under the NATO Science for Peace and Security Programme. The Titan X Pascal GPU used for this research was donated by NVIDIA.
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Oghaz, M.M.D., Argyriou, V., Monekosso, D., Remagnino, P. (2019). Skin Identification Using Deep Convolutional Neural Network. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_14
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DOI: https://doi.org/10.1007/978-3-030-33720-9_14
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