Hybrid Approach for Biometric Recognition: Integrating Custom Vector Quantization and CNN-Based Feature Extraction
Keywords:
Multimodal, Unimodal Biometric System, Kekre’s Median Codebook, Kekre’s Fast Codebook, Feature IntegrationAbstract
A biometric recognition is performed with feature extraction, matching, and classification. Before the emergence of deep learning, biometric recognition has completely relied on manual feature extraction. Convolutional neural networks have automated feature extraction. To fetch features manually, domain knowledge and programming expertise are required. A dataset quality affects accuracy of a shallow classifier whereas the performance of a deep learning model succeeds in providing high accuracy only if the training dataset is balanced, qualitative, and large enough to distinguish features from various classes. Constructing a classifier from a large training dataset is time-consuming and causes overfitting. On the other hand, a small dataset-based model suffers from underfitting. To overcome the said issues, this paper proposed a hybrid approach of a concatenation of manually extracted domain-independent features such as Kekre’s Median Codebook and Kekre’s Fast Codebook and automatically extracted features through CNNs by processing samples from physiological and behavioral biometric traits independently and feeding these to neural networks to achieve best possible accuracy of classification so that the possibility of underfitting and overfitting is avoided. This method is evaluated by applying it to LFW, UPOL, IITD, IITD V1, and UserSignatureDatabase datasets of face, iris, fingerprint, palmprint, and signature respectively, and resulting models achieved improved (certain models achieved equivalent accuracy) with reduced memory and learning time.
Downloads
References
Krishna Dharavath, F. A. Talukdar, R. H. Laskar, “Study on Biometric Authentication Systems, Challenges, and Future Trends: A Review” DOI: 10.1109/ICCIC.2013.6724278
Anil K. Jain, Arun Ross, Salil Prabhakar, “An Introduction to Biometric Recognition”, January 2004. DOI:10.1109/TCSVT.2003.818349
Zhang Rui, Zheng Yan, “A Survey On Biometric Authentication: Towards Secure And Privacy-Preserving Identification” IEEE 2017 DOI: 10.1109/ACCESS.2018.2889996
Hoo-Chang Shin, Holger R. Roth, Mingchen Gao, Le Lu, Ziyue Xu “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning”, 2016 IEEE. https://doi.org/10.1109/TMI.2016.2528162
Monica Bianchini, Franco Scarselli “On the complexity of shallow and deep neural network classifiers”, ESANN 2014 proceedings. DOI: 10.1109/TNNLS.2013.2293637
H. B. Kekre, Tanuja K. Sarode, Bhakti C. Raul, Jan. 2009. “Color Image Segmentation using Kekre’s Fast Codebook Generation Algorithm Based on Energy Ordering Concept”, ACM. https://doi.org/10.1145/1523103.1523175
H B Kekre, T K Sarode, V A Bharadi, A A Agrawal, R J Arora, M C Nair, 26 February 2010. “Iris Recognition Using Discrete Cosine Transform and Vector Quantization”. https://doi.org/10.1145/1741906.1741913
Kitsuchart Pasupa, Wisuwat Sunhem, “A Comparison between Shallow and Deep Architecture Classifiers on Small Dataset”, IEEE 2016. DOI: 10.1109/ICITEED.2016.7863293
Ali Bou Nassif, Abdollah Masoud Darya, Ashraf Elnagar, “Empirical evaluation of shallow and deep learning classifiers for Arabic sentiment analysis”, ACM transactions 2021. https://doi.org/10.1145/3466171
Guk Bae Kim , Kyu-Hwan Jung , Yeha Lee , Hyun-Jun Kim , Namkug Kim , Sanghoon Jun , Joon Beom Seo , David A Lynch,“Comparison of Shallow and Deep Learning Methods on Classifying the Regional Pattern of Diffuse Lung Disease”, J Digit Imaging 2018 DOI:10.1007/s10278-017-0028-9
Xu-Cheng Yin, Chun Yang, Wei-Yi Pei, “Shallow Classification or Deep Learning: An Experimental Study”, IEEE 2014. DOI: 10.1109/ICPR.2014.333
Ali Beikmohammadi, Karim Faez, “Mixture of Deep-based Representation and Shallow Classifiers to Recognize Human Activities”, IEEE 2019. DOI: 10.1109/ICSPIS48872.2019.9066014
G. Willems, T. Tuytelaars, and L. VanGool, “An efficient dense and scale-invariant spatio-temporal interest point detector,” 2008, pp. 650-663: Springer. https://doi.org/10.1007/978-3-540-88688-4_48
A. Klaser, M. Marszałek, and C. Schmid, “A spatio-temporal descriptor based on 3d-gradients,” in BMVC 2008-19th British Machine Vision Conference, 2008 DOI:10.5244/C.22.99
M. Jain, H. Jegou, and P. Bouthemy, “Better exploiting motion for better action recognition”, IEEE 2013. DOI: 10.1109/CVPR.2013.330
A. Fathi, Y. Li, and J. M. Rehg, “Learning to recognize daily actions using gaze,” in European Conference, 2012, https://doi.org/10.1007/978-3-642-33718-5_23.
H. Wang and C. Schmid, “Action recognition with improved trajectories,” IEEE. DOI: 10.1109/ICCV.2013.441
A. B. Sargano, P. Angelov, and Z. Habib, “Human action recognition from multiple views based on view-invariant feature descriptor using support vector machines,” 2016. https://doi.org/10.3390/app6100309
C. Yuan, X. Li, W. Hu, H. Ling, and S. J. Maybank, “Modeling geometric-temporal context with directional pyramid co-occurrence for action recognition,” IEEE 2014.
Le Yang, Dongmei Jiang and Hichem Sahli, “Integrating Deep and Shallow Models for Multi-Modal Depression Analysis — Hybrid Architectures”, IEEE 2018. DOI: 10.1109/TAFFC.2018.2870398
Jacek Haneczok, Jakub Piskorski, “Shallow and deep learning for event relatedness classification”, Elsevier 2020. https://doi.org/10.1016/j.ipm.2020.102371
Boonyawee Grodniyomchai, Khattiya Chalapat, Kulsawasd Jitkajornwanich, Saichon Jaiyen, “A Hybrid of Shallow and Deep Learning for Odor Classification Based on Adaptive Boosting”, IEEE 2019. DOI: 10.1109/ICCSCE47578.2019.9068552
Quang Tri Chiem; Margaret Lech; Richardt H. Wilkinson, “A Hybrid Two-Stage 3D Object Recognition from Orthogonal Projections”, IEEE 2019. DOI: 10.1109/ICSPCS47537.2019.9008740
Takayuki Hoshino, Suguru Kanoga, Masashi Tsubaki, Atsushi Aoyama, “Comparing subject-to-subject transfer learning methods in surface electromyogram-based motion recognition with shallow and deep classifiers”, Elsevier 2022. https://doi.org/10.1016/j.neucom.2021.12.081
Ajay Shrestha, Ausif Mahmood, “Review of Deep Learning Algorithms and Architectures”, IEEE April 2019. DOI: 10.1109/ACCESS.2019.2912200
Fatin Sadiq Alkinani, Abdul Monem S. Rahma February 2019. “A Comparative Study of KMCG Segmentation Based on YCbCr, RGB, and HSV Color Spaces”. DOI:10.29304/jqcm.2019.11.1.468
Dr. Sudeep Thepade, Dimple Parekh, Jinali Shah, Bhumin Shah, Paras Vora, July 2012. “Classification of Fingerprint using KMCG Algorithm”, IJSTR. DOI:10.5120/ijca2018917285
Dr. H B Kekre, T K Sarode, V A Bharadi, Tejas Bajaj, February 26–27, 2010. “A Comparative Study of DCT and Kekre’s Median Code Book Generation Algorithm for Face Recognition”, ACM. https://doi.org/10.1145/1741906.1741961
Dr. H. B. Kekre. Tanuja K. Sarode. Sudeep D. Thepade. Pallavi N. Halarnkar, 2011. “Kekre’s fast codebook generation in VQ with various color spaces for colorization of grayscale images”, Springer India. https://link.springer.com/chapter/10.1007/978-81-8489-989-4_18
Tanuja K. Sarode, Nabanita Mandal, Sept. 2013. “K-Means Codebook Optimization using KFCG Clustering Technique”, International Journal of Computer Applications. DOI:10.5120/13496-1228
Sowmya, et al., “Iris Recognition System for Biometric Identification”, International Journal of Emerging Trends & Technology in Computer Science, (2013). DOI: 10.1109/ICCITECHN.2007.4579354
Nassem, “Iris recognition using class-specific dictionaries”, Computers and Electrical Engineering, (2016). https://doi.org/10.1016/j.compeleceng.2015.12.017
Vahid Nazmdeh, Shaghayegh Mortazavi, Daniel Tajeddin et. el. “Iris Recognition; From Classic to Modern Approaches”, IEEE 2019. DOI: 10.1109/CCWC.2019.8666516
Aguilar, “Fingerprint Recognition”, IEEE 2007. DOI: 10.1007/978-0-387-73003-5_47
Gowthami, “Fingerprint Recognition Using Zone Based Linear Binary Patterns”, IEEE 2015. https://doi.org/10.1016/j.procs.2015.08.072
A. K. Jain and J. Feng, "Latent fingerprint matching", IEEE Jan. 2011. DOI:10.1109/TPAMI.2010.59
M. A. Medina-Pérez et al., "Latent fingerprint identification using deformable minutiae clustering", Neurocomputing, vol. 175, pp. 851-865, Jan. 2016. https://doi.org/10.1016/j.neucom.2015.05.130
K. Cao and A. K. Jain, "Automated latent fingerprint recognition", IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, pp. 788-800, Apr. 2019.
Danilo Valdes-Ramirez et al., “A Review of Fingerprint Feature Representations and Their Applications for Latent Fingerprint Identification: Trends and Evaluation”, IEEE 2019. DOI: 10.1109/ACCESS.2019.2909497
S. Zhao and B. Zhang, ‘‘Joint constrained least-square regression with deep convolutional feature for palmprint recognition,’’ IEEE Jan. 2022. DOI: 10.1109/TSMC.2020.3003021
Dele W. S. Alausa, Emmanuel Adetiba, “Contactless Palmprint Recognition System: A Survey”, IEEE Dec. 2022. DOI: 10.1109/ACCESS.2022.3193382
Lixiang Li1,2,3 Xiaohui Mu1,3 Siying Li 1,3 Haipeng Peng, “A Review of Face Recognition Technology”, IEEE 2020. DOI: 10.1109/ACCESS.2020.301102
Yenneti, L. L. ., Singam, A. ., & Gottapu, S. R. . (2023). Conflicting Parameter Pair Optimization for Linear Aperiodic Antenna Array using Chebyshev Taper based Genetic Algorithm. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1), 161–166. https://doi.org/10.17762/ijritcc.v11i1.6086
Dhabliya, D. (2021). Feature Selection Intrusion Detection System for The Attack Classification with Data Summarization. Machine Learning Applications in Engineering Education and Management, 1(1), 20–25. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/8
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
