Abstract
Optical Coherence Tomography (OCT) is a high resolution imaging technology, which provides a 3D representation of the fingertip skin. This paper for the first time investigates gender classification using those 3D fingerprints. Different with current fingerprint gender classification methods, the raw multiple longitudinal(X-Z) fingertip images of one finger can be applied instead of studying features extracted from fingerprints, and the model can be trained effectively when the training data set is relatively small. Experimental results show that the best accuracy of 80.7% is achieved by classifying left fore finger on a small database with 59 persons. Meanwhile, with the same data size and method, the accuracy of classification based on 3D fingerprints is much higher than that based on 2D fingerprints: the highest accuracy is increased by 46.8%, and the average accuracy is increased by 26.5%.
The work is partially supported by the Natural Science Foundation of China (61672357, 61573248, 61802267, 61732011 and U1713214), the Science and Technology Funding of Guangdong Province (2017A030313367 and 2018A050501014), Shenzhen Fundamental Research fund (JCYJ20180305125822769), the Education Department of Shaanxi Province (15JK1086), and Shaanxi University of Science and Technology Dr. Foundation (BJ14-07).
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Liu, H., Zhang, W., Liu, F., Qi, Y. (2019). 3D Fingerprint Gender Classification Using Deep Learning. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_5
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