Abstract
After a crime scene, accurate gender recognition by fingerprint analysis is vital for detectives because precise gender recognition highly limits the search space. For extracting high quality features from fingerprint images, each image should be preprocessed. The preprocessing stages include segmentation, normalization, filtering, binarization, and thinning. Next, different features from various domains are elicited from each image. The suggested features are ridge count, minutiae points, discrete cosine transform, entropy, local binary pattern and ridge thickness valley thickness ratio features. Each feature and the combination of features for one and five fingers are separately applied to six efficient classifiers for gender recognition. The best result implies 99% accuracy with the ridge count for all five fingers. A combination of features for each finger in the best case provides 91% gender recognition accuracy. The combination of our candidate features for each finger is compared to singular value decomposition (SVD), discrete wavelet transform (DWT), and the combination of SVD and DWT. Our results statistically (p value < 0.05) outperform the compared methods.
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References
Afrasiabi, S., Boostani, R., Masnadi-Shirazi, M. A., & Nezam, T. (2021). An EEG based hierarchical classification strategy to differentiate five intensities of pain. Expert Systems with Applications, 180, 115010.
Alimardani, F., & Boostani, R. (2018). Improvement of the performance of fingerprint verification using a combinatorial approach. Biomedical Engineering: Applications, Basis and Communications, 30(03), 1850019.
Alimardani, F., Rad, N. M., & Boostani, R. (2016). An efficient approach to enhance the performance of fingerprint recognition.
Bahmed, F., & Ould Mammar, M. (2021). Basic finger inner-knuckle print: A new hand biometric modality. IET Biometrics, 10(1), 65–73.
Chand, P., & Sarangi, S. K. (2013). A novel method for gender classification using DWT and SVD techniques. International Journal of Computer Technology and Applications, 4(3), 445.
Deng, S., Huang, Z., Wang, X., & Huang, G. (2017). Radio frequency fingerprint extraction based on multidimension permutation entropy. International Journal of Antennas and Propagation, 2017, 1–6.
Deshmukh, D. K., & Patil, S. S. (2020). Fingerprint-based gender classification by using neural network model (pp. 318–325). Springer.
Deypir, M., Boostani, R., & Zoughi, T. (2012). Ensemble based multi-linear discriminant analysis with boosting and nearest neighbor. Scientia Iranica, 19(3), 654–661.
Effah, A. A., Ackatiah, C. C., Oppong, F. N., & Frimpong, E. A. (2020). Biometric class attendance register. In 2020 IEEE PES/IAS PowerAfrica. IEEE.
Fierrez, J., Ortega-Garcia, J., Toledano, D. T., & Gonzalez-Rodriguez, J. (2007). BioSec baseline corpus: A multimodal biometric database. Pattern Recognition, 40(4), 1389–1392.
Gnanasivam, P., & Muttan, D. S. (2012). Fingerprint gender classification using wavelet transform and singular value decomposition. arXiv preprint arXiv:1205.6745
Gornale, S., Patil, A., & Veersheety, C. (2016). Fingerprint based gender identification using discrete wavelet transform and gabor filters. International Journal of Computer Applications, 975, 8887.
Hassan Mohamed Hassan, M. (2019). Verify identity using fingerprint identification. Cooperation with Motorola.
Iloanusi, O. N., & Ejiogu, U. C. (2020). Gender classification from fused multi-fingerprint types. Information Security Journal: A Global Perspective, 29(5), 209–219.
Jayakala, G. (2021). Gender classification based on fingerprint analysis. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(10), 1249–1256.
Joshi, M., Joshi, V. B., & Raval, M. S. (2013). Multilevel semi-fragile watermarking technique for improving biometric fingerprint system security. In International conference on intelligent interactive technologies and multimedia. Springer.
Kant, C., & Chaudhary, S. (2021). A multimodal biometric system based on finger knuckle print, fingerprint, and palmprint traits. In Innovations in computational intelligence and computer vision (Proceedings of ICICV) (pp. 182–192).
Kaur, R., & Mazumdar, S. G. (2012). Fingerprint based gender identification using frequency domain analysis. International Journal of Advances in Engineering & Technology, 3(1), 295.
Kho, J. B., Teoh, A. B., Lee, W., & Kim, J. (2020). Bit-string representation of a fingerprint image by normalized local structures. Pattern Recognition, 103, 107323.
Lei, W., & Lin, Y. (2020). A novel dynamic fingerprint segmentation method based on fuzzy c-means and genetic algorithm. IEEE Access, 8, 132694–132702.
Militello, C. (2021). Fingerprint classification based on deep learning approaches: Experimental findings and comparisons. Symmetry, 13(5), 750.
Moayedi, F., Azimifar, Z., Boostani, R., & Katebi, S. (2010). Contourlet-based mammography mass classification using the SVM family. Computers in Biology and Medicine, 40(4), 373–383.
Muhammed, A., & Pais, A. R. (2020). A novel fingerprint image enhancement based on super resolution. In 2020 6th international conference on advanced computing and communication systems (ICACCS). IEEE.
Nagabhyru, S. (2016). Gender estimation from fingerprints using DWT and entropy. MSc. Thesis, West Virginia University.
Nebli, A., & Rekik, I. (2020). Adversarial brain multiplex prediction from a single brain network with application to gender fingerprinting. Medical Image Analysis, 67, 101843.
Peralta, D., Galar, M., Triguero, I., Paternain, D., García, S., Barrenechea, E., Benítez, J. M., Bustince, H., & Herrera, F. (2015). A survey on fingerprint minutiae-based local matching for verification and identification: Taxonomy and experimental evaluation. Information Sciences, 315, 67–87.
Rim, B., Kim, J., & Hong, M. (2020). Gender classification from fingerprint-images using deep learning approach. In Proceedings of the international conference on research in adaptive and convergent systems.
Sabeti, M., Boostani, R., & Davoodi, B. (2017). Improved particle swarm optimisation to estimate bone age. IET Image Processing, 12(2), 179–187.
Sharifnia, E., & Boostani, R. (2020). Instance-based cost-sensitive boosting. International Journal of Pattern Recognition and Artificial Intelligence, 34(03), 2050002.
Shinde, M. K., & Annadate, S. (2015). Analysis of fingerprint image for gender classification or identification: Using wavelet transform and singular value decomposition. In 2015 international conference on computing communication control and automation. IEEE.
Sujatha, E., Sundar, J. S. J., Deivendran, P., & Indumathi, G. (2021). Multimodal biometric algorithm using IRIS, finger vein, finger print with hybrid GA (pp. 267–283). Springer.
Tarare, S., Anjikar, A., & Turkar, H. (2015). Fingerprint based gender classification using DWT transform. In 2015 international conference on computing communication control and automation. IEEE.
Tuncer, T., & Dogan, S. (2019). A novel octopus based Parkinson’s disease and gender recognition method using vowels. Applied Acoustics, 155, 75–83.
Wang, Z., Hou, Z., Wang, Z., Li, X., Wei, B., Lv, X., & Yang, T. (2020). Identification system based on fingerprint and finger vein. In International conference on computer engineering and networks. Springer.
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Jalali, S., Boostani, R. & Mohammadi, M. Efficient fingerprint features for gender recognition. Multidim Syst Sign Process 33, 81–97 (2022). https://doi.org/10.1007/s11045-021-00789-6
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DOI: https://doi.org/10.1007/s11045-021-00789-6