Abstract
Large-scale data-driven DNN models have been proven to achieve great performance in various computer vision challenges, and transfer learning is proposed recently to take advantage of pre-trained DNN on a small database. Under such a framework, an innovative classification model for both identity and gender classification with hand vein information is proposed in this paper. By adopting pre-trained VGG and AlexNet model with ImageNet database and the corresponding fine-tuned ones with PolyU fingerprint and palmprint database, state-of-the-art classification results are obtained with the fine-tuned ones, which indicates that domain-specific model performs better than a generic one, and similar experimental results with faces further indicate that biometric traits share latent patterns. On the other hand, to evaluate the distribution of shared patterns, a quantized shared-index calculated as the number of correlated dictionary atoms is realized based on a sparse representation model.
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Shen, Z., Wang, J., Wang, G., Pan, Z. (2021). Biometric Traits Share Patterns. In: Sun, F., Liu, H., Fang, B. (eds) Cognitive Systems and Signal Processing. ICCSIP 2020. Communications in Computer and Information Science, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-2336-3_37
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DOI: https://doi.org/10.1007/978-981-16-2336-3_37
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