Fingerprint Image Fusion: A Cutting-edge Perspective on Gender Classification via Rotational Invariant Features

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Author(s)

Shivanand Gornale 1 Abhijit Patil 2 Khang Wen Goh 3,4 Sathish Kumar 1,* Kruthi R 5

1. Department of Computer Science, Rani Channamma University, Belagavi-591156, Karnataka, India

2. Department of Computer Science, Gitam University, Hyderabad-502329, Telangana, India

3. Faculty of Data Science and Information Technology, INTI International University, 71800 Nilai, Malaysia

4. School of Engineering and Technology, Shinawatra University, Thailand

5. Department of Computer Science, Maharaja Institute of Technology, Mysore-571477, Karnataka, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2024.04.04

Received: 7 Dec. 2023 / Revised: 12 Jan. 2024 / Accepted: 16 Mar. 2024 / Published: 8 Aug. 2024

Index Terms

Biometrics, Fingerprint, Feature Fusion, Gender Classification, Image Fusion

Abstract

In this cutting-edge technological milieu, fingerprints have become an alternative expression for the biometrics system. A fingerprint is one of the perceptible biometric modals which is predominantly utilized in almost all the security, and real-life applications. Fingerprints have many inherent rotational features that are mostly utilized for person recognition besides these features can also be utilized for the person gender classification. Thus, the proposed work is a novel algorithm which identifies the gender of an individual based on the fingerprint. The image fusion and feature level fusion technique are deliberated over the fingerprints with rotational invariant features. Experiments were carried on four state-of-the-art datasets and realized promising results by outperforming earlier outcomes.

Cite This Paper

Shivanand Gornale, Abhijit Patil, Khang Wen Goh, Sathish Kumar, Kruthi R. , "Fingerprint Image Fusion: A Cutting-edge Perspective on Gender Classification via Rotational Invariant Features", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.16, No.4, pp. 42-55, 2024. DOI:10.5815/ijigsp.2024.04.04

Reference

[1]Ma, J., Ma, Y., & Li, C. (2019). Infrared and visible image fusion methods and applications: A survey. Information Fusion, 45, 153–178. https://doi.org/10.1016/j.inffus.2018.02.004 
[2]Gornale, S. S., Hangarge, M., Pardeshi, R., & Kruthi, R. (2015). Haralick feature descriptors for gender classification using fingerprints: A machine learning approach. International Journal of Advanced Research in Computer Science and Software Engineering, 5(9), 72-78. 
[3]R, K., Patil, A., & Gornale, S. (2019). Fusion of Features and Synthesis Classifiers for Gender Classification using Fingerprints. International Journal of Computer Sciences and Engineering, 7(5), 526–533. https://doi.org/10.26438/ijcse/v7i5.526533  
[4]De-La-Torre, M., Granger, E., Radtke, P. V. W., Sabourin, R., & Gorodnichy, D. O. (2015). Partially-supervised learning from facial trajectories for face recognition in video surveillance. Information Fusion, 24, 31–53. https://doi.org/10.1016/j.inffus.2014.05.006  
[5]Tapia, J. E., Perez, C. A., & Bowyer, K. W. (2015). Gender classification from iris images using fusion of uniform local binary patterns. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8926, pp. 751–763). Springer Verlag. https://doi.org/10.1007/978-3-319-16181-5_57 
[6]Gornale, S., Patil, A., & Hangarge, M. (2021). Palmprint Biometric Data Analysis for Gender Classification Using Binarized Statistical Image Feature Set (pp. 157–167). https://doi.org/10.1007/978-981-16-1681-5_1 
[7]Gornale, S. S., Kumar, S., Patil, A., & Hiremath, P. S. (2021). Behavioral Biometric Data Analysis for Gender Classification Using Feature Fusion and Machine Learning. Frontiers in Robotics and AI, 8. https://doi.org/10.3389/frobt.2021.685966 
[8]Djemili, R., Bourouba, H., & Korba, M. C. A. (2012). A speech signal based gender identification system using four classifiers. In Proceedings of 2012 International Conference on Multimedia Computing and Systems, ICMCS 2012 (pp. 184–187). https://doi.org/10.1109/ICMCS.2012.6320122  
[9]Mayhew, S. (2015). History of Biometrics. Biometric Update. Retrieved from http://www.biometricupdate.com/201501/history-of-biometrics 
[10]Brunelli, R., & Falavigna, D. (1995). Person Identification Using Multiple Cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(10), 955–966. https://doi.org/10.1109/34.464560 
[11]Marasco, E., Lugini, L., & Cukic, B. (2014). Exploiting quality and texture features to estimate age and gender from fingerprints. In Biometric and Surveillance Technology for Human and Activity Identification XI (Vol. 9075, p. 90750F). SPIE. https://doi.org/10.1117/12.2048125  
[12]Gornale, S. S. (2015). Fingerprint based gender classification for biometric security: A state-of-the-art technique. AIJRSTEM, 9(1), 39-49. 
[13]Gornale, S. S., Basavanna, M., & Kruthi, R. (2015). Gender classification using fingerprints based on support vector machines (SVM) with 10-cross validation technique. International Journal of Scientific & Engineering Research, 6(7), 588-593.
[14]Gornale, S. (2017). Fingerprint Based Gender Classification Using Local Binary Pattern. International Journal of Computational Intelligence Research ISSN 0973-1873 Volume 13, Number 2 (2017), Pp. 261-271, 13(2), 261–271. 
[15]S.S, G., & R, K. (2014). Fusion of Fingerprint and Age Biometric for Gender Classification Using Frequency and Texture Analysis. Signal & Image Processing: An International Journal, 5(6), 75–85. https://doi.org/10.5121/sipij.2014.5606 
[16]Gornale, S., Patil, A., & C., V. (2016). Fingerprint based Gender Identification using Discrete Wavelet Transform and Gabor Filters. International Journal of Computer Applications, 152(4), 34–37. https://doi.org/10.5120/ijca2016911794 
[17]Gornale, S. S., & Patil, A. (2016). Statistical Features Based Gender Identification Using SVM. International Journal for Scientific Research and Development, (8), 241-244. 
[18]R, K., Patil, A., & Gornale, S. (2019). Fusion of Local Binary Pattern and Local Phase Quantization features set for Gender Classification using Fingerprints. International Journal of Computer Sciences and Engineering, 7(1), 22–29. https://doi.org/10.26438/ijcse/v7i1.2229 
[19]Alam, S., Dipti, Dua, M., & Gupta, A. (2019). A comparative study of gender classification using fingerprints. In Proceedings of the 2019 6th International Conference on Computing for Sustainable Global Development, INDIACom 2019 (pp. 880–884). Institute of Electrical and Electronics Engineers Inc. 
[20]Deshmukh, D. K., & Patil, S. S. (2020). Fingerprint-based gender classification by using neural network model. In Applied Computer Vision and Image Processing: Proceedings of ICCET 2020, Volume 1 (pp. 318-325). Springer Singapore. 
[21]Rim, B., Kim, J., & Hong, M. (2020). Gender Classification from Fingerprint-images using Deep Learning Approach. In ACM International Conference Proceeding Series (pp. 7–12). Association for Computing Machinery. https://doi.org/10.1145/3400286.3418237 
[22]Qi, Y., Qiu, M., Jiang, H., & Wang, F. (2022). Extracting Fingerprint Features Using Autoencoder Networks for Gender Classification. Applied Sciences (Switzerland), 12(19). https://doi.org/10.3390/app121910152 
[23]Tom, R. J., & Arulkumaran, T. (2013). Fingerprint Based Gender Classification Using 2D Discrete Wavelet Transforms and Principal Component Analysis. International Journal of Engineering Trends and Technology, 4(2), 199–203. 
[24]Tan, X., & Triggs, B. (2007, October). Fusing Gabor and LBP feature sets for kernel-based face recognition. In International workshop on analysis and modeling of faces and gestures (pp. 235-249). Berlin, Heidelberg: Springer Berlin Heidelberg. 
[25]Gornale, S. S., Patravali, P. U., & Hiremath, P. S. (2020). Automatic Detection and Classification of Knee Osteoarthritis Using Hu’s Invariant Moments. Frontiers in Robotics and AI, 7. https://doi.org/10.3389/frobt.2020.591827 
[26]Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 29(1), 51–59. https://doi.org/10.1016/0031-3203(95)00067-4 
[27]Gornale, S. S., Patil, A., & Kruthi, R. (2019). Multimodal biometrics data based gender classification using machine vision. International Journal of Innovative Technology and Exploring Engineering, 8(11), 1356–1363. https://doi.org/10.35940/ijitee.J9673.0981119 
[28]Baskar, A., Rajappa, M., Vasudevan, S. K., & Murugesh, T. S. (2023). Digital Image Processing. Digital Image Processing (pp. 1–193). CRC Press. https://doi.org/10.1201/9781003217428 
[29]Gornale, S. S., Patil, A., & Hangarge, M. (2019, March). Binarized Statistical Image Feature set for Palmprint based Gender Identification. In Book of abstract-International Conference on Machine Learning, Image Processing, Network Security and Data Sciences (MIND-2019) pp (Vol. 61, pp. 3-4).
[30]Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Transactions on pattern analysis and machine intelligence, 24(7), 971-987.
[31]Huang, Z., & Leng, J. (2010). Analysis of Hu’s moment invariants on image scaling and rotation. In ICCET 2010 - 2010 International Conference on Computer Engineering and Technology, Proceedings (Vol. 7). https://doi.org/10.1109/ICCET.2010.5485542 
[32]Hu, M. K. (1962). Visual Pattern Recognition by Moment Invariants. IRE Transactions on Information Theory, 8(2), 179–187. https://doi.org/10.1109/TIT.1962.1057692 
[33]Karimi-Ashtiani, S., & Jay Kuo, C. C. (2008). A robust technique for latent fingerprint image segmentation and enhancement. In Proceedings - International Conference on Image Processing, ICIP (pp. 1492–1495). https://doi.org/10.1109/ICIP.2008.4712049 
[34]James, A. P., & Dasarathy, B. V. (2014). Medical image fusion: A survey of the state of the art. Information Fusion, 19(1), 4–19. https://doi.org/10.1016/j.inffus.2013.12.002 
[35]Liu, Y., Wang, L., Cheng, J., Li, C., & Chen, X. (2020). Multi-focus image fusion: A Survey of the state of the art. Information Fusion, 64, 71–91. https://doi.org/10.1016/j.inffus.2020.06.013
[36]Grigor’eva, M. A. (2019). Human gender determination based on the measurements of handprints that are devoid of dermatoglyphic features. Sudebno-Meditsinskaya Ekspertiza, 62(4), 22–29. https://doi.org/10.17116/sudmed20196204122 
[37]Yin, Y., Liu, L., & Sun, X. (2011). SDUMLA-HMT: A multimodal biometric database. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7098 LNCS, pp. 260–268). https://doi.org/10.1007/978-3-642-25449-9_33 
[38]Siddiqui, A. M., Telgad, R. L., Lothe, S. A., & Deshmukh, P. D. (2019). Development of Secure Multimodal Biometric System for Person Identification Using Feature Level Fusion: Fingerprint and Iris. In Recent Trends in Image Processing and Pattern Recognition: Second International Conference, RTIP2R 2018, Solapur, India, December 21–22, 2018, Revised Selected Papers, Part II 2 (pp. 406-432). Springer Singapore.
[39]Sharanappa Gornale, S., Patil, A., & Ramchandra, K. (2020). Multimodal Biometrics Data Analysis for Gender Estimation Using Deep Learning. International Journal of Data Science and Analysis, 6(2), 64. https://doi.org/10.11648/j.ijdsa.20200602.11