Human Recognition Using Ear Features: A Review
DOI:
https://doi.org/10.29304/jqcm.2023.15.2.1232Keywords:
Biometrics, SIFT, CNN, Deep LearningAbstract
Over the past few years, ear biometrics has attracted a lot of attention. It is a trusted biometric for the identification and recognition of humans due to its consistent shape and rich texture variation. The ear presents an attractive solution since it is visible, ear images are easily captured, and the ear structure remains relatively stable over time. In this paper, a comprehensive review of prior research was conducted to establish the efficacy of utilizing ear features for individual identification through the employment of both manually-crafted features and deep-learning approaches. The objective of this model is to present the accuracy rate of person identification systems based on either manually-crafted features such as DCT, DWT, DFT, PCA, LBP, SURF, SIFT, etc., or deep learning techniques such as CNN, DNN, Alex Net CNN, VGG-16, SVM, Squeeze Net, Google Net, MobileNetV2, etc. The effort will make it easier for researchers, especially those who are new to the field, to have a brief understanding of the trend of employing deep learning in a trustworthy biometric for the identification and recognition of human identification.
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References
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[2] M. A. Rajab and K. M. Hashim, "Dorsal hand veins features extraction and recognition by correlation coefficient," TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 20, no. 4, pp. 867-874, 2022.
[3] S. Prakash and P. Gupta, "An efficient ear recognition technique invariant to illumination and pose," Telecommunication Systems, vol. 52, pp. 1435-1448, 2013.
[4] M. A. Rajab and L. E. George, "An Efficient Method for Stamps Recognition Using Histogram Moment with Haar Wavelet Sub-bands," Iraqi Journal of Science, pp. 3182-3195, 2021.
[5] M. A. Rajab and L. E. George, "An Efficient Method for Stamps Verification Using Haar Wavelet Sub-bands with Histogram and Moment," in 2021 1st Babylon International Conference on Information Technology and Science (BICITS), 2021, pp. 120-126: IEEE.
[6] A. Kohlakala and J. Coetzer, "Ear-based biometric authentication through the detection of prominent contours," SAIEE Africa Research Journal, vol. 112, no. 2, pp. 89-98, 2021.
[7] P. Srivastava, D. Agarwal, and A. Bansal, "Ear based human identification using a combination of wavelets and multi-scale local binary pattern," International Journal of Future Generation Communication and Networking, vol. 12, no. 3, pp. 41-56, 2019.
[8] Ž. Emeršič, V. Štruc, and P. Peer, "Ear recognition: More than a survey," Neurocomputing, vol. 255, pp. 26-39, 2017.
[9] B. A. ABDULGHANI and A. K. AL-SULAIFANIE, "EAR RECOGNITION USING LOCAL BINARY PATTERN," Journal of Duhok University, pp. 120-128, 2017.
[10] H. Nguyen Quoc and V. Truong Hoang, "Real-time human ear detection based on the joint of yolo and retinaface," Complexity, vol. 2021, pp. 1-11, 2021.
[11] T. Ying, Z. Debin, and Z. Baihuan, "Ear recognition based on weighted wavelet transform and DCT," in The 26th Chinese Control and Decision Conference (2014 CCDC), 2014, pp. 4410-4414: IEEE.
[12] A. Basit and M. Shoaib, "A human ear recognition method using nonlinear curvelet feature subspace," International Journal of Computer Mathematics, vol. 91, no. 3, pp. 616-624, 2014.
[13] K. Annapurani, M. Sadiq, and C. Malathy, "Fusion of shape of the ear and tragus–a unique feature extraction method for ear authentication system," Expert Systems with Applications, vol. 42, no. 1, pp. 649-656, 2015.
[14] L. Ghoualmi, A. Draa, and S. Chikhi, "Ear feature extraction using a dwt-sift hybrid," in Intelligent Data Analysis and Applications: Proceedings of the Second Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2015, 2015, pp. 37-47: Springer.
[15] I. Omara, F. Li, H. Zhang, and W. Zuo, "A novel geometric feature extraction method for ear recognition," Expert Systems with Applications, vol. 65, pp. 127-135, 2016.
[16] A. S. Anwar, K. K. A. Ghany, and H. ElMahdy, "Human ear recognition using SIFT features," in 2015 Third World Conference on Complex Systems (WCCS), 2015, pp. 1-6: IEEE.
[17] A. M. Mayya and M. Saii, "Human recognition based on ear shape images using PCA-Wavelets and different classification methods," Med Devices Diagn Eng: DOI, vol. 10, 2016.
[18] M. M. Zarachoff, A. Sheikh-Akbari, and D. Monekosso, "Non-decimated wavelet based multi-band ear recognition using principal component analysis," IEEE Access, vol. 10, pp. 3949-3961, 2021.
[19] M. Arunachalam and S. B. Alagarsamy, "An efficient ear recognition system using DWT & BLPOC," in 2017 International conference on inventive communication and computational technologies (ICICCT), 2017, pp. 16-19: IEEE.
[20] R. N. Othman, F. Alizadeh, and A. Sutherland, "A novel approach for occluded ear recognition based on shape context," in 2018 International Conference on Advanced Science and Engineering (ICOASE), 2018, pp. 93-98: IEEE.
[21] S. M. Jiddah and K. Yurtkan, "Fusion of geometric and texture features for ear recognition," in 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2018, pp. 1-5: IEEE.
[22] N. Aishna Sharma and M. Mani Roja, "Edinburgh.(2019). Biometric Identification using Human Ear," International Journal of Engineering and Advanced Technology (IJEAT), vol. 9.
[23] A. Hassin and D. Abbood, "Machine Learning System for Human–Ear Recognition Using Scale Invariant Feature Transform," Artificial Intelligence & Robotics Development Journal, pp. 1-12, 2021.
[24] A. M. Radhi and S. A. Mohammed, "Enhancement Ear-based Biometric System Using a Modified AdaBoost Method," Baghdad Science Journal, vol. 19, no. 6, pp. 1346-1346, 2022.
[25] J. Jeyabharathi, S. Devi, B. Krishnan, R. Samuel, M. I. Anees, and R. Jegadeesan, "Human Ear Identification System Using Shape and structural feature based on SIFT and ANN Classifier," in 2022 International Conference on Communication, Computing and Internet of Things (IC3IoT), 2022, pp. 01-06: IEEE.
[26] I. Omara, X. Wu, H. Zhang, Y. Du, and W. Zuo, "Learning pairwise SVM on hierarchical deep features for ear recognition," IET Biometrics, vol. 7, no. 6, pp. 557-566, 2018.
[27] N. Jamil, A. Almisreb, S. Ariffin, N. M. Din, and R. Hamzah, "Can convolution neural network (CNN) triumph in ear recognition of uniform illumination invariant?," Indonesian Journal of Electrical Engineering and Computer Science, vol. 11, no. 2, pp. 558-66, 2018.
[28] S. Dodge, J. Mounsef, and L. Karam, "Unconstrained ear recognition using deep neural networks," IET Biometrics, vol. 7, no. 3, pp. 207-214, 2018.
[29] A. Abd Almisreb, N. Jamil, and N. M. Din, "Utilizing AlexNet deep transfer learning for ear recognition," in 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP), 2018, pp. 1-5: IEEE.
[30] F. I. Eyiokur, D. Yaman, and H. K. Ekenel, "Domain adaptation for ear recognition using deep convolutional neural networks," iet Biometrics, vol. 7, no. 3, pp. 199-206, 2018.
[31] W. Raveane, P. L. Galdámez, and M. A. González Arrieta, "Ear detection and localization with convolutional neural networks in natural images and videos," Processes, vol. 7, no. 7, p. 457, 2019.
[32] H. Sinha, R. Manekar, Y. Sinha, and P. K. Ajmera, "Convolutional neural network-based human identification using outer ear images," in Soft Computing for Problem Solving: SocProS 2017, Volume 2, 2019, pp. 707-719: Springer.
[33] A. Tomczyk and P. S. Szczepaniak, "Ear detection using convolutional neural network on graphs with filter rotation," Sensors, vol. 19, no. 24, p. 5510, 2019.
[34] H. Alshazly, C. Linse, E. Barth, and T. Martinetz, "Ensembles of deep learning models and transfer learning for ear recognition," Sensors, vol. 19, no. 19, p. 4139, 2019.
[35] S. El-Naggar and T. Bourlai, "Evaluation of deep learning models for ear recognition against image distortions," in 2019 European Intelligence and Security Informatics Conference (EISIC), 2019, pp. 85-93: IEEE.
[36] R. Ahila Priyadharshini, S. Arivazhagan, and M. Arun, "A deep learning approach for person identification using ear biometrics," Applied intelligence, vol. 51, pp. 2161-2172, 2021.
[37] A. M. Alkababji and O. H. Mohammed, "Real time ear recognition using deep learning," TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 19, no. 2, pp. 523-530, 2021.
[38] Y. Lei, J. Qian, D. Pan, and T. Xu, "Research on small sample dynamic human ear recognition based on deep learning," Sensors, vol. 22, no. 5, p. 1718, 2022.
[39] M. Sharkas, "Ear recognition with ensemble classifiers; A deep learning approach," Multimedia Tools and Applications, pp. 1-27, 2022.
[40] S. Ramos-Cooper, P. Arequipa, and G. Camara-Chavez, "Domain Adaptation for Unconstrained Ear Recognition with Convolutional Neural Networks," CLEI electronic journal, vol. 25, no. 2, 2022.
[41] T. Ebanesar, A. Bibin, and J. Jalaja, "HUMAN EAR RECOGNITION USING CONVOLUTIONAL NEURAL NETWORK," Journal of Positive School Psychology, pp. 8182-8190, 2022.
[42] K. Resmi, G. Raju, V. Padmanabha, and J. Mani, "Person Identification by Models Trained Using Left and Right Ear Images Independently," in 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022), 2023, pp. 281-288: Atlantis Press.
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Published
2023-09-24
How to Cite
Rajab, M. A., & Hashim, K. M. (2023). Human Recognition Using Ear Features: A Review. Journal of Al-Qadisiyah for Computer Science and Mathematics, 15(2), Comp Page 51–62. https://doi.org/10.29304/jqcm.2023.15.2.1232
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Computer Articles
