A Comprehensive survey on ear recognition: Databases, approaches, comparative analysis, and open challenges

Neurocomputing - Tập 537 - Trang 236-270 - 2023
Amir Benzaoui1, Yacine Khaldi2, Rafik Bouaouina3, Nadia Amrouni4, Hammam Alshazly5, Abdeldjalil Ouahabi6
1Electrical Engineering Department, University of Skikda, BP 26, El Hadaiek, Skikda 21000, Algeria
2Computer Sciences Department, University of Kasdi Merbah, Ouargla 30000, Algeria
3PIMIS Laboratory, Electronics and Telecommunications Department, University of May 8, 1945, Guelma 24000, Algeria
4SETL Laboratory, Electrical Systems Engineering Department, University of Boumerdes, Boumerdes, 35000, Algeria
5Faculty of Computers and Information, South Valley University, Qena 83523, Egypt
6UMR 1253, iBrain, INSERM, Universite de Tours, Tours 37000, France

Tài liệu tham khảo

Dargan, 2020, A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities, Expert Systems with Applications, 143, 10.1016/j.eswa.2019.113114 D. Biometrics, The helix software developer kit (Accessed on 20/10/2022). URL: http://www.descartesbiometrics.com/helix-sdk. L.B. Baldwin, Ear recognition as device input, uS Patent 9,049,983 (Jun. 9 2015). Sforza, 2009, Age-and sex-related changes in the normal human ear, Forensic science international, 187, 110 Yoga, 2017, Assessment of age changes and gender differences based on anthropometric measurements of ear: A cross-sectional study, Journal of Advanced Clinical & Research Insights, 4, 92, 10.15713/ins.jcri.167 Pflug, 2015 Toygar, 2020, On the use of ear and profile faces for distinguishing identical twins and nontwins, Expert Systems, 37, 12389, 10.1111/exsy.12389 Pun, 2004, Recent advances in ear biometrics, 164 Choras, 2007, Image feature extraction methods for ear biometrics–a survey, 261 Ramesh, 2009, Pattern extraction methods for ear biometrics-a survey, 1657 Kurniawan, 2012, A review on 2d ear recognition, 204 Pflug, 2012, Ear biometrics: a survey of detection, feature extraction and recognition methods, IET biometrics, 1, 114, 10.1049/iet-bmt.2011.0003 Abaza, 2013, A survey on ear biometrics, ACM computing surveys, 45, 1, 10.1145/2431211.2431221 Emeršič, 2017, Ear recognition: More than a survey, Neurocomputing, 255, 26, 10.1016/j.neucom.2016.08.139 Alva, 2019, A review on techniques for ear biometrics, 1 Srivastava, 2019, Ear detection and recognition techniques: a comparative review, 533 Wang, 2021, Review of ear biometrics, Archives of Computational Methods in Engineering, 28, 149, 10.1007/s11831-019-09376-2 Kamboj, 2021, A comprehensive survey and deep learning-based approach for human recognition using ear biometric, The Visual Computer, 34 Hezil, 2017, Multimodal biometric recognition using human ear and palmprint, IET Biometrics, 6, 351, 10.1049/iet-bmt.2016.0072 El-Naggar, 2016, On a taxonomy of ear features, 1 Ross, 2011, Human ear recognition, Computer, 44, 79, 10.1109/MC.2011.344 Bertillon, 1890 Iannarelli, 1989 Sundar, 2021, Evaluation of human ear anatomy and functionality by axiomatic design, Biomimetics, 6, 31, 10.3390/biomimetics6020031 Ibrahim, 2011, The effect of time on ear biometrics, 1 Mukhopadhyay, 2015, A survey of hough transform, Pattern Recognition, 48, 993, 10.1016/j.patcog.2014.08.027 Sánchez-Cruz, 2007, Efficiency of chain codes to represent binary objects, Pattern Recognition, 40, 1660, 10.1016/j.patcog.2006.10.013 Chen, 2002, A pseudo top-hat mathematical morphological approach to edge detection in dark regions, Pattern Recognition, 35, 199, 10.1016/S0031-3203(01)00024-3 Ganapathi, 2020, Unconstrained ear detection using ensemble-based convolutional neural network model, Concurrency and Computation: Practice and Experience, 32, 10.1002/cpe.5197 N.Q. H, T.H. V, Real-time human ear detection based on the joint of yolo and retinaface, Complexity (2021). doi:10.1155/2021/7918165. URL: 10.1155/2021/7918165. Zhang, 2017, Ear detection under uncontrolled conditions with multiple scale faster region-based convolutional neural networks, Symmetry, 9, 53, 10.3390/sym9040053 Ren, 2015, Faster r-cnn: Towards real-time object detection with region proposal networks, Advances in neural information processing systems, 28 Emeršič, 2018, Convolutional encoder–decoder networks for pixel-wise ear detection and segmentation, IET Biometrics, 7, 175, 10.1049/iet-bmt.2017.0240 Ž. Emeršič, L.L. Gabriel, V. Štruc, P. Peer, Pixel-wise ear detection with convolutional encoder-decoder networks, arXiv preprint arXiv:1702.00307 (2017). Tomczyk, 2019, Ear detection using convolutional neural network on graphs with filter rotation, Sensors, 19, 5510, 10.3390/s19245510 Emeršič, 2021, Contexednet: Context–aware ear detection in unconstrained settings, IEEE Access, 9, 145175, 10.1109/ACCESS.2021.3121792 Yuan, 2014, Ear recognition based on gabor features and kfda, The Scientific World Journal, 2014, 10.1155/2014/702076 Pflug, 2014, Segmentation and normalization of human ears using cascaded pose regression, 261 Dollár, 2010, Cascaded pose regression, 1078 Kumar, 2012, Ear biometrics in human identification system, International Journal of Information Technology and Computer Science, 2, 41, 10.5815/ijitcs.2012.02.06 Hansley, 2018, Employing fusion of learned and handcrafted features for unconstrained ear recognition, IET Biometrics, 7, 215, 10.1049/iet-bmt.2017.0210 Zhang, 2017, 3d ear normalization and recognition based on local surface variation, Applied Sciences, 7, 104, 10.3390/app7010104 Revina, 2021, A survey on human face expression recognition techniques, Journal of King Saud University-Computer and Information Sciences, 33, 619, 10.1016/j.jksuci.2018.09.002 Adjabi, 2020, Past, present, and future of face recognition: A review, Electronics, 9, 1188, 10.3390/electronics9081188 Giot, 2013, Fast computation of the performance evaluation of biometric systems: Application to multibiometrics, Future Generation Computer Systems, 29, 788, 10.1016/j.future.2012.02.003 U.s.t.b.ear dataset, available at: (2002). URL: http://www1.ustb.edu.cn/resb/en/index.htm. Liu, 2016, The chongqing university chinese ear video database and its application, Pattern Recognition and Image Analysis, 26, 360, 10.1134/S1054661816020061 U. database, C. E, available at: (2002). [link]. URL: http://www.nd.edu/cvrl/CVRL/DataSets.html. Yan, 2007, Biometric recognition using 3d ear shape, IEEE Transactions on pattern analysis and machine intelligence, 29, 1297, 10.1109/TPAMI.2007.1067 Iit delhi ear dataset, available at: (2007). URL: https://www4.comp.polyu.edu.hk/ csajaykr/IITD/Database_Ear.htm. E. Gonzalez, Ami ear database, available at: (2008). URL: http://www.ctim.es/research_works/ami_ear_database. Alshazly, 2019, Ensembles of deep learning models and transfer learning for ear recognition, Sensors, 19, 4139, 10.3390/s19194139 Xiang, 2009, Design and construction of chinese ear image database, Computer Engineering, 35, 275 Prakash, 2013, An efficient ear recognition technique invariant to illumination and pose, Telecommunication Systems, 52, 1435, 10.1007/s11235-011-9621-2 Frejlichowski, 2010, The west pomeranian university of technology ear database – a tool for testing biometric algorithms, 227 Raposo, 2011, Ubear: A dataset of ear images captured on-the-move in uncontrolled conditions, 84 Emeršič, 2015, Ear biometric database in the wild, 27 Zhou, 2017, Deformable models of ears in-the-wild for alignment and recognition, 626 Parkhi, 2015, Deep face recognition, 411 Zhang, 2017, Ustb-helloear: A large database of ear images photographed under uncontrolled conditions, 405 E.Ž, The unconstrained ear recognition challenge, in: 2017 IEEE international joint conference on biometrics (IJCB), Denver, CO, USA, 2017, p. 715–724. doi:10.1109/BTAS.2017.8272761. URL: https://doi.org/10.1109/BTAS.2017.8272761 Emeršič, 2018, Evaluation and analysis of ear recognition models: performance, complexity and resource requirements, Neural computing and applications, 32, 15785, 10.1007/s00521-018-3530-1 Ž, 2019, The unconstrained ear recognition challenge 2019, 1 V. Hoang, Earvn1.0: A new large-scale ear images dataset in the wild, Data in brief 27 (2019) 104630. doi:10.1016/j.dib.2019.104630. URL: 10.1016/j.dib.2019.104630. Ramos-Cooper, 2022, Vggface-ear: An extended dataset for unconstrained ear recognition, Sensors, 22, 1752, 10.3390/s22051752 Q. Cao, L. Shen, W. Xie, O. Parkhi, A. Zisserman, Vggface2: A dataset for recognizing faces across pose and age, 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG) (2018) 67–74. Chen, 2008, Efficient recognition of highly similar 3d objects in range images, IEEE Transactions on Pattern analysis and machine intelligence, 31, 172, 10.1109/TPAMI.2008.176 Guillon, 2012, Creating the sydney york morphological and acoustic recordings of ears database, 461 Jin, 2013, Creating the sydney york morphological and acoustic recordings of ears database, IEEE Transactions on Multimedia, 16, 37, 10.1109/TMM.2013.2282134 Liu, 2015, Ear-parotic face angle: A unique feature for 3d ear recognition, Pattern Recognition Letters, 53, 9, 10.1016/j.patrec.2014.10.014 Liu, 2016, Online 3d ear recognition by combining global and local features, Plos one, 11, 0166204, 10.1371/journal.pone.0166204 Ganapathi, 2018, 3d ear recognition using global and local features, IET Biometrics, 7, 232, 10.1049/iet-bmt.2017.0212 Dai, 2018, A data-augmented 3d morphable model of the ear, 404 Turk, 1991, Eigenfaces for recognition, Journal of cognitive neuroscience, 3, 71, 10.1162/jocn.1991.3.1.71 Belhumeur, 1997, Eigenfaces vs. fisherfaces: Recognition using class specific linear projection, IEEE Transactions on pattern analysis and machine intelligence, 19, 711, 10.1109/34.598228 Zhang, 2005, A novel approach for ear recognition based on ica and rbf network, 4511 Xie, 2007, Improved locally linear embedding and its application on multi-pose ear recognition, 1367 Yuan, 2007, Ear recognition based on 2d images, 1 Zhang, 2008, Ear recognition method based on fusion features of global and local features, 347 Huang, 2011, Ear recognition based on uncorrelated local fisher discriminant analysis, Neurocomputing, 74, 3103, 10.1016/j.neucom.2011.04.022 Yuan, 2012, Ear recognition based on local information fusion, Pattern Recognition Letters, 33, 182, 10.1016/j.patrec.2011.09.041 Hanmandlu, 2013, Robust ear based authentication using local principal independent components, Expert Systems with Applications, 40, 6478, 10.1016/j.eswa.2013.05.020 Haneche, 2019, New mobile communication system design for rayleigh environments based on compressed sensing-source coding, IET Communications, 13, 2375, 10.1049/iet-com.2018.5348 Haneche, 2020, A new way to enhance speech signal based on compressed sensing, Measurement, 151, 10.1016/j.measurement.2019.107117 Haneche, 2021, Compressed sensing-speech coding scheme for mobile communications, Circuits, Systems, and Signal Processing, 40, 5106, 10.1007/s00034-021-01712-x Mahdaoui, 2022, Image denoising using a compressive sensing approach based on regularization constraints, Sensors, 22, 2199, 10.3390/s22062199 Zhang, 2015, A survey of sparse representation: algorithms and applications, IEEE access, 3, 490, 10.1109/ACCESS.2015.2430359 Yuan, 2006, Ear recognition using improved non-negative matrix factorization, 501 Yuan, 2012, Ear recognition under partial occlusion based on sparse representation, 349 Banerjee, 2016, Image set based ear recognition using novel dictionary learning and classification scheme, Engineering Applications of Artificial Intelligence, 55, 37, 10.1016/j.engappai.2016.05.005 Yuan, 2016, Non-negative dictionary based sparse representation classification for ear recognition with occlusion, Neurocomputing, 171, 540, 10.1016/j.neucom.2015.06.074 Banerjee, 2017, Robust multimodal multivariate ear recognition using kernel based simultaneous sparse representation, Engineering Applications of Artificial Intelligence, 64, 340, 10.1016/j.engappai.2017.06.011 Hurley, 2000, Automatic ear recognition by force field transformations, 1 Abate, 2006, Ear recognition by means of a rotation invariant descriptor, 437 Chan, 2012, Reliable ear identification using 2-d quadrature filters, Pattern Recognition Letters, 33, 1870, 10.1016/j.patrec.2011.11.013 Basit, 2014, A human ear recognition method using nonlinear curvelet feature subspace, International Journal of Computer Mathematics, 91, 616, 10.1080/00207160.2013.800194 Meraoumia, 2015, An automated ear identification system using gabor filter responses, 1 Ghoualmi, 2016, An ear biometric system based on artificial bees and the scale invariant feature transform, Expert Systems with Applications, 57, 49, 10.1016/j.eswa.2016.03.004 Chowdhury, 2018, On applicability of tunable filter bank based feature for ear biometrics: a study from constrained to unconstrained, Journal of medical systems, 42, 1, 10.1007/s10916-017-0855-8 Chowdhury, 2019, Semantic ear feature reduction for source camera identification, Multimedia Tools and Applications, 79, 35315 Chatterjee, 2019, Ear biometrics recognition using laser biospeckled fringe projection profilometry, Optics & Laser Technology, 112, 368, 10.1016/j.optlastec.2018.11.043 Mangayarkarasi, 2019, Contour detection based ear recognition for biometric applications, Procedia Computer Science, 165, 751, 10.1016/j.procs.2020.01.016 Chowdhury, 2020, Wavelet energy feature based source camera identification for ear biometric images, Pattern Recognition Letters, 130, 139, 10.1016/j.patrec.2018.10.009 M. Burge, W. Burger, Ear biometrics in computer vision, in: 2000 15th International Conference on Pattern Recognition (ICPR), Barcelona, Spain, 2000, p. 822–826. doi:10.1109/ICPR.2000.906202. URL: doi: 10.1109/ICPR.2000.906202. Moreno, 1999, On the use of outer ear images for personal identification in security applications, 469 Choras, 2006, Geometrical algorithms of ear contour shape representation and feature extraction, 451 Rahman, 2013, Human ear recognition using geometric features, 1 Xu, 2012, Ear recognition based on centroid and spindle, Procedia Engineering, 29, 2162, 10.1016/j.proeng.2012.01.280 Sibai, 2013, Ear recognition with feed-forward artificial neural networks, Neural Computing and Applications, 23, 1265, 10.1007/s00521-012-1068-1 Lakshmanan, 2013, Efficient person authentication based on multi-level fusion of ear scores, IET biometrics, 2, 97, 10.1049/iet-bmt.2012.0049 Anwar, 2015, Human ear recognition using geometrical features extraction, Procedia Computer Science, 65, 529, 10.1016/j.procs.2015.09.126 Omara, 2016, A novel geometric feature extraction method for ear recognition, Expert Systems with Applications, 65, 127, 10.1016/j.eswa.2016.08.035 Zarachoff, 2022, Chainlet-based ear recognition using image multi-banding and support vector machine, Applied Sciences, 12, 2033, 10.3390/app12042033 Adjabi, 2021, Multi-block color-binarized statistical images for single-sample face recognition, Sensors, 21, 728, 10.3390/s21030728 Djeddi, 2010, Discrete wavelet for multifractal texture classification: Application to medical ultra sound imaging, 637 Khaldi, 2019, Combining colour and grey-level co-occurrence matrix features: a comparative study, IET Image Processing, 13, 1401, 10.1049/iet-ipr.2018.6440 Guo, 2008, Ear recognition using a new local matching approach, 289 Benzaoui, 2014, Ear biometric recognition using local texture descriptors, Journal of electronic imaging, 23, 10.1117/1.JEI.23.5.053008 Benzaoui, 2017, Experiments and improvements of ear recognition based on local texture descriptors, Optical Engineering, 56, 10.1117/1.OE.56.4.043109 Youbi, 2019, Human ear recognition based on local multi-scale lbp features with city-block distance, Multimedia Tools and Applications, 78, 14425, 10.1007/s11042-018-6768-9 A.R. MM, M. ML, G. M, O. E, L. B, M. A, A dense phase descriptor for human ear recognition, IEEE Access 6 (2018) 11883–11887. doi:10.1109/ACCESS.2018.2810339. URL: https://doi.org/10.1109/ACCESS.2018.2810339 Hassaballah, 2019, Ear recognition using local binary patterns: A comparative experimental study, Expert Systems with Applications, 118, 182, 10.1016/j.eswa.2018.10.007 Hassaballah, 2020, Robust local oriented patterns for ear recognition, Multimedia Tools and Applications, 79, 31183, 10.1007/s11042-020-09456-7 Sarangi, 2020, An evaluation of ear biometric system based on enhanced jaya algorithm and surf descriptors, Evolutionary Intelligence, 13, 443, 10.1007/s12065-019-00311-9 Omara, 2020, Ldm-dagsvm: Learning distance metric via dag support vector machine for ear recognition problem, 1 Arbaoui, 2021, Concrete cracks detection and monitoring using deep learning-based multiresolution analysis, Electronics, 10, 1772, 10.3390/electronics10151772 Jia, 2021, A survey: Deep learning for hyperspectral image classification with few labeled samples, Neurocomputing, 448, 179, 10.1016/j.neucom.2021.03.035 Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, 25, 1097 S. C, Going deeper with convolutions, in: 2015 IEEE conference on computer vision and pattern recognition (CVPR), Boston, MA, 2015, p. 1–9. doi:10.1109/CVPR.2015.7298594. URL: https://doi.org/10.1109/CVPR.2015.7298594 K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition (2014). URL: arXiv:1409.1556. He, 2016, Deep residual learning for image recognition, 770 F. Iandola, S. Han, M. Moskewicz, K. Ashraf, W. Dally, K. Keutzer, Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size (2016). URL: doi: 10.1016/j.neucom.2020.10.081. Wang, 2021, Deep face recognition: A survey, Neurocomputing, 429, 215, 10.1016/j.neucom.2020.10.081 Galdámez, 2017, A brief review of the ear recognition process using deep neural networks, Journal of Applied Logic, 24, 62, 10.1016/j.jal.2016.11.014 Omara, 2018, Learning pairwise svm on hierarchical deep features for ear recognition, IET Biometrics, 7, 557, 10.1049/iet-bmt.2017.0087 Dodge, 2018, Unconstrained ear recognition using deep neural networks, IET Biometrics, 7, 207, 10.1049/iet-bmt.2017.0208 Zhang, 2018, Ear verification under uncontrolled conditions with convolutional neural networks, IET Biometrics, 7, 185, 10.1049/iet-bmt.2017.0176 Eyiokur, 2018, Domain adaptation for ear recognition using deep convolutional neural networks, IET Biometrics, 7, 199, 10.1049/iet-bmt.2017.0209 Alshazly, 2019, Handcrafted versus cnn features for ear recognition, Symmetry, 11, 1493, 10.3390/sym11121493 Alshazly, 2020, Deep convolutional neural networks for unconstrained ear recognition, IEEE Access, 8, 170295, 10.1109/ACCESS.2020.3024116 Emeršič, 2017, Training convolutional neural networks with limited training data for ear recognition in the wild, 987 Zhang, 2019, Few-shot learning for ear recognition, 50 Tan, 2018, A survey on deep transfer learning, 270 Priyadharshini, 2021, A deep learning approach for person identification using ear biometrics, Applied Intelligence, 51, 2161, 10.1007/s10489-020-01995-8 Khaldi, 2021, A new framework for grayscale ear images recognition using generative adversarial networks under unconstrained conditions, Evolving Systems, 12, 923, 10.1007/s12530-020-09346-1 Khaldi, 2021, Ear recognition based on deep unsupervised active learning, IEEE Sensors Journal, 21, 20704, 10.1109/JSEN.2021.3100151 Alshazly, 2021, Towards explainable ear recognition systems using deep residual networks, IEEE Access, 9, 122254, 10.1109/ACCESS.2021.3109441 Korichi, 2022, Tr-icanet: A fast unsupervised deep-learning-based scheme for unconstrained ear recognition, Arabian Journal for Science and Engineering, 12 Saleh, 2009, Hybrid features of spatial domain and frequency domain for person identification through ear biometrics, Pattern Recognition and Image Analysis, 19, 35, 10.1134/S1054661809010052 Kumar, 2012, Automated human identification using ear imaging, Pattern Recognition, 45, 956, 10.1016/j.patcog.2011.06.005 Murukesh, 2012, A novel ear recognition process using appearance shape model, fisher linear discriminant analysis and contourlet transform, Procedia engineering, 38, 771, 10.1016/j.proeng.2012.06.097 Ahmed, 2015, Nonparametric denoising methods based on contourlet transform with sharp frequency localization: Application to low exposure time electron microscopy images, Entropy, 17, 3461, 10.3390/e17053461 Kumar, 2013, Robust ear identification using sparse representation of local texture descriptors, Pattern recognition, 46, 73, 10.1016/j.patcog.2012.06.020 Breiman, 1995, Better subset regression using the nonnegative garrote, Technometrics, 37, 373, 10.1080/00401706.1995.10484371 Galdámez, 2016, A small look at the ear recognition process using a hybrid approach, Journal of Applied Logic, 17, 4, 10.1016/j.jal.2015.09.004 Guermoui, 2016, Weighted sparse representation for human ear recognition based on local descriptor, Journal of Electronic Imaging, 25 Guermoui, 2016, Sparse coding joint decision rule for ear print recognition, Optical Engineering, 55, 10.1117/1.OE.55.9.093105 Chen, 2016, Partial data ear recognition from one sample per person, IEEE Transactions on Human-Machine Systems, 46, 799, 10.1109/THMS.2016.2598763 Omara, 2018, Metric learning with dynamically generated pairwise constraints for ear recognition, Information, 9, 215, 10.3390/info9090215 Alagarsamy, 2020, Ear recognition system using adaptive approach runge–kutta (aark) threshold segmentation with anfis classification, Neural Computing and Applications, 32, 10995, 10.1007/s00521-018-3805-6 Alagarsamy, 2020, Ear recognition system using adaptive approach runge-kutta (aark) threshold segmentation with cart classifier, Multimedia Tools and Applications, 79, 10445, 10.1007/s11042-019-7418-6 Doghmane, 2019, A novel discriminant multiscale representation for ear recognition, International Journal of Biometrics, 11, 50, 10.1504/IJBM.2019.096568 Sarangi, 2019, Fusion of phog and ldp local descriptors for kernel-based ear biometric recognition, Multimedia Tools and Applications, 78, 9595, 10.1007/s11042-018-6489-0 Sajadi, 2020, Genetic algorithm based local and global spectral features extraction for ear recognition, Expert Systems with Applications, 159, 10.1016/j.eswa.2020.113639 Boujnah, 2020, Ear recognition in degraded conditions based on spectral saliency: smart home access, Journal of Electronic Imaging, 29, 10.1117/1.JEI.29.2.023024 Ouahabi, 2013 Z. Wang, X. Gao, J. Yang, Q. Yan, Y. Zhang, Local feature fusion and src-based decision fusion for ear recognition. multimedia systems (2022). doi:10.1007/s00530-022-00906-w. URL: doi: 10.1007/s00530-022-00906-w. Kacar, 2019, Scorenet: deep cascade score level fusion for unconstrained ear recognition, IET Biometrics, 8, 109, 10.1049/iet-bmt.2018.5065 Khaldi, 2020, Region of interest synthesis using image-to-image translation for ear recognition, 1 I. Omara, A. Hagag, G. Ma, F. Abd El-Samie, E. Song, A novel approach for ear recognition: learning mahalanobis distance features from deep cnns, Machine Vision and Applications 32 (1) (2021) 1–14. doi:10.1007/s00138-020-01155-5. URL: doi: 10.1007/s00138-020-01155-5. Ganapathi, 2022, A survey of 3d ear recognition techniques, ACM Computing Surveys (CSUR) Yan, 2005, Icp-based approaches for 3d ear recognition, 282 Chen, 2005, Contour matching for 3d ear recognition, 123 Chen, 2007, Human ear recognition in 3d, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 718, 10.1109/TPAMI.2007.1005 Sun, 2014, 3d ear recognition using local salience and principal manifold, Graphical models, 76, 402, 10.1016/j.gmod.2014.03.003 Zhang, 2017, 3d ear normalization and recognition based on local surface variation, Applied Sciences, 7, 104, 10.3390/app7010104 Ganapathi, 2017, 3d ear based human recognition using gauss map clustering, 83 A. Ganapathi, II, P. SS, S., Geometric statistics-based descriptor for 3d ear recognition, The Visual Computer 36 (1) (2020) 161–173. doi:10.1007/s00371-018-1593-8. URL: doi: 10.1007/s00371-018-1593-8. Dave, 2018, 3d ear biometrics: acquisition and recognition, 1 Zhu, 2018, An efficient 3d ear recognition system based on indexing, 507 D. Zhang, G. Lu, L. Zhang, Online 3D Ear Recognition, Advanced Biometrics, Springer, In, 2018. doi:10.1007/978-3-319-61545-5_14. URL: https://doi.org/10.1007/978-3-319-61545-5_14 Ganapathi, 2019, Multi-resolution local descriptor for 3d ear recognition, 221 Yan, 2006, An automatic 3d ear recognition system, 326 S. Islam, R. Davies, A. Mian, M. Bennamoun, A fast and fully automatic ear recognition approach based on 3d local surface features, in: 2008 International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS), Juan-les-Pins, France, 2008, p. 1081–1092. doi:10.1007/978-3-540-88458-3_98. URL: doi: 10.1007/978-3-540-88458-3_98. Prakash, 2014, Human recognition using 3d ear images, Neurocomputing, 140, 317, 10.1016/j.neucom.2014.03.007 Gower, 1975, Generalized procrustes analysis, Psychometrika, 40, 33, 10.1007/BF02291478 Chen, 2017, 102250 P. Ganapathi, II, D. S, J. IR, A. P, S. SS, A.M., Ear recognition in 3d using 2d curvilinear features, IET Biometrics 7 (6) (2018) 519–529. doi:10.1049/iet-bmt.2018.5064. URL: https://doi.org/10.1049/iet-bmt.2018.5064 Claes, 2015, An investigation of matching symmetry in the human pinnae with possible implications for 3d ear recognition and sound localization, Journal of Anatomy, 226, 60, 10.1111/joa.12252 A. Abaza, A. Ross, Towards understanding the symmetry of human ears: A biometric perspective, Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) (2010) 1–7 doi:10.1109/BTAS.2010.5634535. URL: doi: 10.1109/BTAS.2010.5634535. Toygar, 2018, Symmetric ear and profile face fusion for identical twins and non-twins recognition, Signal, Image and Video Processing, 12, 1157, 10.1007/s11760-018-1263-3 Nejati, 2012, Wonder ears: Identification of identical twins from ear images, 1201 P. Yan, K. Bowyer, Empirical evaluation of advanced ear biometrics, in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)-Workshops, San Diego, CA, USA, 2005, p. 41–41. doi:10.1109/CVPR.2005.450. URL: doi: 10.1109/CVPR.2005.450. Meng, 2021, On distinctiveness and symmetry in ear biometrics, IEEE Transactions on Biometrics, Behavior, and Identity Science, 3, 155, 10.1109/TBIOM.2021.3058562 Toprak, 2021, Detection of spoofing attacks for ear biometrics through image quality assessment and deep learning, Expert Systems with Applications, 172, 10.1016/j.eswa.2021.114600 Toprak, 2020, Ear anti-spoofing against print attacks using three-level fusion of image quality measures, Signal, Image and Video Processing, 14, 417, 10.1007/s11760-019-01570-w Sepas-Moghaddam, 2018, Ear presentation attack detection: Benchmarking study with first lenslet light field database, 2355 Nourmohammadi-Khiarak, 2018, An ear anti-spoofing database with various attacks, 1 Lei, 2015, Automatic ear landmark localization, segmentation, and pose classification in range images, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46, 165, 10.1109/TSMC.2015.2452892 Kamboj, 2021, Ced-net: context-aware ear detection network for unconstrained images, Pattern Analysis and Applications, 24, 779, 10.1007/s10044-020-00914-4 Raveane, 2019, Ear detection and localization with convolutional neural networks in natural images and videos, Processes, 7, 457, 10.3390/pr7070457 Ganapathi, 2020, Unconstrained ear detection using ensemble-based convolutional neural network model, Concurrency and Computation, Practice and Experience, 32, 5197, 10.1002/cpe.5197 Emeršič, 2021, Contexednet: Context–aware ear detection in unconstrained settings, IEEE Access, 9, 145175, 10.1109/ACCESS.2021.3121792 Wang, 2022, A survey on kinship verification, Neurocomputing, 10.3389/978-2-88974-540-1 Wu, 2022, Facial kinship verification: A comprehensive review and outlook, International Journal of Computer Vision, 1 Meng, 2019, Gender and kinship by model-based ear biometrics, 1 Dvoršak, 2022, Kinship verification from ear images: An explorative study with deep learning models, 1