Abstract
Palmprint recognition system is a promising biometric technology which received extremely large interest of researches. Many different algorithms and systems have been proposed and built. Although, great success has been achieved in palmprint research, however, the accuracy and spoofing mechanism are limited in some cases, as the palmprint feature may be similar for a given spectral illumination; hyperspectral Palmprint is a good recognition method to address this issue, it can provide more discriminate information under different illumination in a short time. Optimal band selection is a very important step to building hyperspectral palmprint recognition system. In this paper, for dimensionality reduction 2D2 LDA, is used on pre-processed palmprint images, for feature extraction 2D Gabor filter is being used, for pattern matching SVM, KNN classifiers are used. Experiment result showed that 2 spectral bands 700 nm and 960 nm could provide the highest recognition accuracy for hyperspectral palmprint recognition.
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Acknowledgement
We would like to thank The Hong Kong Polytechnic University (PolyU) for sharing their database (PolyU Hyperspectral palmprint Database). This project is under UGC Maulana Azad National Fellowship, F1-17.1/2017-18/MANF-2017-18-MAH-82272/(SA-III/Website) sanction at Department of Computer Science & IT, Dr. Babasaheb Ambedkar Marathawada University.
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Khandizod, A.G., Deshmukh, R.R. (2019). Optimal Band Selection for Improvement of Hyperspectral Palmprint Recognition System by Using SVM and KNN Classifier. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore. https://doi.org/10.1007/978-981-13-9184-2_38
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DOI: https://doi.org/10.1007/978-981-13-9184-2_38
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