Abstract
In the current trend towards contactless recognition, palmprint biometrics has proven to be very effective due to its uniqueness, reliability, acceptability, non-intrusiveness and low cost of acquisition devices. Palmprint biometrics can be used in many situations by simply capturing the hand with the camera of a mobile device. However, its use in real-life situations adds a high variability to the capturing conditions, increasing the complexity of the recognition process and causing failure of many processing methods up to date. In this study, a deep-learning algorithm is proposed to detect the palmprint region of interest and rotating the image as needed for the subsequent feature extraction on it. For this purpose, a convolutional neural network (CNN) has been trained and evaluated with 2445 hand images from 6 different databases that cover diverse environmental conditions. Results show that this algorithm provides an averaged F 1-score of 89% in images with complex backgrounds, dim light or varied hand arrangements; and it correctly processes images in which users are wearing rings, something that traditional segmentation cannot handle. Some conditions such as hard shadowing remain very complex for this algorithm, but it could be highly improved by increasing the volume of training datasets.
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Acknowledgement
The authors would like to thank Hong Kong Polytechnic University for the use of PolyU 2D Palmprint Database as well as all the contributors who have participated in the creation of our proprietary databases for their patience and cooperation.
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Sánchez, B.M., Sánchez, B.R., Ávila, C.S. (2022). Deep Learning for Palmprint Detection and Alignment on Biometric Systems. In: Daimi, K., Francia III, G., Encinas, L.H. (eds) Breakthroughs in Digital Biometrics and Forensics. Springer, Cham. https://doi.org/10.1007/978-3-031-10706-1_13
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