Human Recognition based on Multi-instance Ear Scheme
DOI:
https://doi.org/10.47839/ijc.22.3.3236Keywords:
ear recognition, local features, SIFT, feature selection, GA, BPAbstract
Ear biometrics is one of the primary biometrics that is definitely standing out. Ear recognition enjoys special benefits and can make distinguishing proof safer and dependable along with other biometrics (for example fingerprints and face). Particularly as a supplement to face recognition schemes that experience issues in genuine circumstances. This is because of the extraordinary variety of a planar representation of a confusing object that is varied in shapes, illumination, and profile shape. This study is an endeavor to conquer these restrictions, by proposing scale-invariant feature transform (SIFT) calculation to extract feature vector descriptors from both left and right ears which is to be melded as one descriptor utilized for verification purposes. Likewise, another plan is proposed for the recognition stage, based on a genetic algorithm-backpropagation neural network as an accurate recognition approach. This approach will be tried by utilizing images from the Indian Institute of Technology Delhi's creation (IITD). The suggested system exhibits a 99.7% accuracy recognition rate.
References
H. Inass Shahadha, S. Shamsul Bin, N. Md Jan, and A. Nilam Nur, “The extract region of interest in high-resolution palmprint using 2D image histogram entropy function,” Journal of Computer Science, vol. 15, no. 5, 2019. https://doi.org/10.3844/jcssp.2019.635.647.
S. B. Alagarsamy and S. Kondappan, “Ear recognition system using adaptive approach Runge–Kutta (AARK) threshold segmentation with ANFIS classification,” Neural Computing and Applications, vol. 32, no. 15, pp. 10995-11006, 2020. https://doi.org/10.1007/s00521-018-3805-6.
Z. Wang, J. Yang, and Y. Zhu, “Review of ear biometrics,” Archives of Computational Methods in Engineering, vol. 28, no. 1, pp. 149-180, 2021. https://doi.org/10.1007/s11831-019-09376-2.
A. V. Iannarelli, The Iannarelli System of Ear Identification, Foundation Press, 1964.
S. O. Berg, “Sherlock Holmes: father of scientific crime and detection,” J. Crim. L. Criminology & Police Sci., vol. 61, p. 446, 1970. https://doi.org/10.2307/1141973.
S. M. Jiddah and K. Yurtkan, “Fusion of geometric and texture features for ear recognition,” Proceedings of the 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2018, pp. 1-5. https://doi.org/10.1109/ISMSIT.2018.8567044,
L. Li, B. Zhong, C. Hutmacher, Y. Liang, W. J. Horrey, and X. Xu, “Detection of driver manual distraction via image-based hand and ear recognition,” Accident Analysis & Prevention, vol. 137, p. 105432, 2020. https://doi.org/10.1016/j.aap.2020.105432,
P. L. Galdámez, W. Raveane, and A. González Arrieta, “A brief review of the ear recognition process using deep neural networks,” Journal of Applied Logic, vol. 24, pp. 62-70, 2017. https://doi.org/10.1016/j.jal.2016.11.014.
R. Agarwal, “Local and global features based on ear recognition system,” Proceedings of the International Conference on Artificial Intelligence and Sustainable Engineering, Springer Singapore, 2022, pp. 477-486. https://doi.org/10.1007/978-981-16-8546-0_39.
M. H. Mutar, E. H. Ahmed, M. R. M. ALsemawi, H. O. Hanoosh, and A. H. Abbas, “Ear recognition system using random forest and histograms of oriented gradients techniques,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 27, no. 1, pp. 181-188, 2022. https://doi.org/10.11591/ijeecs.v27.i1.pp181-188.
M. Sharkas, “Ear recognition with ensemble classifiers: A deep learning approach,” Multimedia Tools and Applications, vol. 81, no. 30, pp. 43919-43945, 2022. https://doi.org/10.1007/s11042-022-13252-w.
D. ping Tian, “A review on image feature extraction and representation techniques,” International Journal of Multimedia and Ubiquitous Engineering, vol. 8, no. 4, pp. 385-396, 2013.
I. S. Hussein, S. Sahibuddin, and N. N. A. Sjarif, “Shape feature extraction methods based pattern recognition: A survey,” Open International Journal of Informatics (OIJI), pp. 26-41, 2018.
M. Hassaballah, H. A. Alshazly, and A. A. Ali, “Ear recognition using local binary patterns: A comparative experimental study,” Expert Systems with Applications, vol. 118, pp. 182-200, 2019. https://doi.org/10.1016/j.eswa.2018.10.007.
N. Boodoo-Jahangeer and S. Baichoo, “LBP-based ear recognition,” Proceedings of the 13th IEEE International Conference on BioInformatics and BioEngineering, 2013, pp. 1-4. https://doi.org/10.1109/BIBE.2013.6701687.
Y. Guo and Z. Xu, “Ear recognition using a new local matching approach,” Proceedings of the 2008 15th IEEE International Conference on Image Processing, 2008, pp. 289-292.
A. Benzaoui, A. Kheider, and A. Boukrouche, “Ear description and recognition using ELBP and wavelets,” Proceedings of the 2015 IEEE International Conference on Applied Research in Computer Science and Engineering (ICAR), 2015, pp. 1-6. https://doi.org/10.1109/ARCSE.2015.7338146.
Y. Zhang, Z. Mu, L. Yuan, H. Zeng, and L. Chen, “3D ear normalization and recognition based on local surface variation,” Applied Sciences, vol. 7, no. 1, p. 104, 2017. https://doi.org/10.3390/app7010104.
L. Yuan, W. Liu, and Y. Li, “Non-negative dictionary based sparse representation classification for ear recognition with occlusion,” Neurocomputing, vol. 171, pp. 540-550, 2016. https://doi.org/10.1016/j.neucom.2015.06.074.
A. Benzaoui, A. Hadid, and A. Boukrouche, “Ear biometric recognition using local texture descriptors,” Journal of electronic imaging, vol. 23, no. 5, p. 053008, 2014. https://doi.org/10.1117/1.JEI.23.5.053008.
A. S. Anwar, K. K. A. Ghany, and H. ElMahdy, “Human ear recognition using SIFT features,” Proceedings of the 2015 IEEE Third World Conference on Complex Systems (WCCS), 2015, pp. 1-6. https://doi.org/10.1109/ICoCS.2015.7483254.
L. Ghoualmi, A. Draa, and S. Chikhi, “An ear biometric system based on artificial bees and the scale invariant feature transform,” Expert Systems with Applications, vol. 57, pp. 49-61, 2016. https://doi.org/10.1016/j.eswa.2016.03.004.
G. Mawloud and M. Djamel, “Weighted sparse representation for human ear recognition based on local descriptor,” Journal of Electronic Imaging, vol. 25, no. 1, p. 013036, 2016. https://doi.org/10.1117/1.JEI.25.1.013036.
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94.
E. A. Elgallad, N. Charfi, A. M. Alimi, and W. Ouarda, “Human identity recognition using sparse auto encoder for texture information representation in palmprint images based on voting technique,” Proceedings of the 2017 Sudan Conference on Computer Science and Information Technology (SCCSIT), 2017, pp. 1-8. https://doi.org/10.1109/SCCSIT.2017.8293060.
A. Hassin, D. Abbood, “Machine learning system for human ear recognition using scale invariant feature transform,” Artificial Intelligence & Robotics Development Journal (AIRDJ), vol. 1, issue 1, 2021. https://doi.org/10.52098/airdj.20217.
I. S. Hussein, S. B. Sahibuddin, M. J. Nordin, and N. N. B. A. Sjarif, “Multimodal recognition system based on high-resolution palmprints,” IEEE Access, vol. 8, pp. 56113-56123, 2020. https://doi.org/10.1109/ACCESS.2020.2982048.
M. I. Ahmad, W. L. Woo, and S. Dlay, “Non-stationary feature fusion of face and palmprint multimodal biometrics,” Neurocomputing, vol. 177, pp. 49-61, 2016. https://doi.org/10.1016/j.neucom.2015.11.003.
S. Srivastava, “Accurate human recognition by score-level and feature-level fusion using palm–phalanges print,” Arabian Journal for Science and Engineering, vol. 43, no. 2, pp. 543-554, 2018. https://doi.org/10.1007/s13369-017-2644-6.
L. D. Vignolo and M. F. Gerard, “Evolutionary local improvement on genetic algorithms for feature selection,” Proceedings de XLIII CLEI–46 JAIIO, pp. 1-8, 2017. https://doi.org/10.1109/CLEI.2017.8226467.
M. Mahalakshmi, P. Kalaivani, and E. K. Nesamalar, “A review on genetic algorithm and its applications,” International Journal of Computing Algorithm, vol. 2, no. 2, pp. 415-423, 2013.
S. Skansi, Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence, Springer, 2018. https://doi.org/10.1007/978-3-319-73004-2.
D. Yashomati, K. Madhuri, S. Pooja, and A. Varsha, “The human identification system using geometrical feature extraction of ear,” International Journal of Advanced Research in Computer Science and Software Engineering IJARCSSE, vol. 6, issue 3, pp. 298-303, 2016.
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