Gotowe bibliografie tematyczne / Ear biometric

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Gotowa bibliografia na temat „Ear biometric”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Ear biometric"

R., Parimala, and Jayakumar C. "Ear Biometrics- An Alternative Biometric." International Journal of Computer Science and Engineering Communications 1, no. 1 (2013): 54–61. https://doi.org/10.5281/zenodo.821762.

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This paper is one of the parts of a biometric based identity verification security system development project. Today, the most successful biometric based identification technologies such as fingerprint and iris scan are used worldwide in both criminal investigations and high security facilities. Even though Face recognition is one of the developing biometric methods; illumination, makeup, posing, emotional expressions and face-lifting reduce the success of face recognition. A new biometric which is not effected by any of the factors above is needed. The alternative biometric should overcome th

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Ali, Nadir, M. Asghar Khattak, Samina Kanwal, Noreen Farid, Shehrbano Batool, and Mufassar Nishat. "Comparison of Forensic Value of Biometric Analysis in Face & Ear Recognition in the Punjabi Population, Pakistan." Pakistan Journal of Medical and Health Sciences 16, no. 12 (2022): 614–16. http://dx.doi.org/10.53350/pjmhs20221612614.

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Background: The use of face biometrics is very prevalent in forensic investigations for the identification of the perpetrators of crime due to the excessive use of CCTV footage that is usually available at the scene of a crime in urban settings. Ear biometric analysis of ear prints is also in vogue as a result of research advancements in the fields of biometrics. Keeping in view this scenario, it is the need of the hour to analyze the forensic value of this type of forensic evidence and compare these two tools of forensic biometrics. Purpose: To analyze and compare the forensic value of biomet

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Shilpi Sharma, Swapnita Srivastava, Vipul Narayan, Rahul Sawhney,. "Ear Biometry: Protection Safeguarding Ear Acknowledgment Framework utilizing Transfer Learning in Industry 4.0." Journal of Electrical Systems 20, no. 3s (2024): 1397–412. http://dx.doi.org/10.52783/jes.1517.

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Human-Recognition using biometric features proposes a noble way of uniquely identifying individuals considering they do not require people to keep loads of passwords in mind to prove their identity. Biometrics have aided in people unable to render other’s identity as well and have advanced over the years. Identification using ear biometric technique is considered to outrun other features since passive human involvement and ease-of-access are its strong set of attributes, not seen in any other biometric techniques. It has managed to identify criminals in the Crime Branch and has various helpful

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Akhilesh Singh and Dr. Mayur Rahul. "Comparative Review of Earlier Research on Multimodal Biometric Authentication Using Ear and Palm Print." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 09 (2024): 2286–90. http://dx.doi.org/10.47392/irjaeh.2024.0312.

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Biometric authentication systems have gained prominence due to their ability to provide secure and reliable identity verification. Among various biometric traits, ear and palmprint recognition have shown significant potential due to their unique and stable features. This review paper explores the integration of ear and palmprint biometrics in a multimodal authentication system, examining their individual characteristics, advantages, challenges, and the synergistic benefits of their combination. We also discuss recent advancements, methodologies, and future directions in multimodal biometric au

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Kumar, Girish, and Dr Ajay Khushwaha. "A modal for better authentication using hybrid biometrics by adding a side face with an ear." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 3 (2020): 2484–92. http://dx.doi.org/10.61841/turcomat.v11i3.14255.

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Customer support in digital environment is most important by facilitating secure authentication and verification for both customer and retail stores. Ear and side face as hybrid biometric is effective and efficient method for authentication and recognition in biometrics. Many researchers reported that ear can be a biometric trait and has sufficient potential to be considered as biometric characteristic and same for the face it has been proved. In this research paper we investigated, with help of MATLAB tool. Idea of multiple traits of ear and side face biometric technique in retail stores for

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Nagrale, Niraj K. "Deep Learning Approaches for Ear Biometrics: A Novel Approach." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem36217.

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This paper presents a novel deep learning model for ear biometrics, achieving state-of-the-art performance through the integration of transfer learning and data augmentation. Ear biometrics has garnered significant interest due to the ear's unique and stable characteristics, making it a viable modality for biometric identification. Traditional methods often falter under variations in lighting, pose, and occlusion, but deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown exceptional capability in overcoming these challenges by learning robust and discriminativ

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Booysens, Aimee, and Serestina Viriri. "Exploration of Ear Biometrics Using EfficientNet." Computational Intelligence and Neuroscience 2022 (August 31, 2022): 1–14. http://dx.doi.org/10.1155/2022/3514807.

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Biometrics is the recognition of a human using biometric characteristics for identification, which may be physiological or behavioral. The physiological biometric features are the face, ear, iris, fingerprint, and handprint; behavioral biometrics are signatures, voice, gait pattern, and keystrokes. Numerous systems have been developed to distinguish biometric traits used in multiple applications, such as forensic investigations and security systems. With the current worldwide pandemic, facial identification has failed due to users wearing masks; however, the human ear has proven more suitable

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Alex, Deepthy Mary, Kalpana Chowdary M., Hanan Abdullah Mengash, et al. "DANNET: deep attention neural network for efficient ear identification in biometrics." PeerJ Computer Science 10 (December 18, 2024): e2603. https://doi.org/10.7717/peerj-cs.2603.

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Biometric identification, particularly ear biometrics, has gained prominence amidst the global prevalence of mask-wearing, exacerbated by the COVID-19 outbreak. This shift has highlighted the need for reliable biometric systems that can function effectively even when facial features are partially obscured. Despite numerous proposed convolutional neural network (CNN) based deep learning techniques for ear detection, achieving the expected efficiency and accuracy remains a challenge. In this manuscript, we propose a sophisticated method for ear biometric identification, named the encoder-decoder

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Ayoub, Rahila. "Study of Ear Biometrics Based Identification System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 188–94. http://dx.doi.org/10.22214/ijraset.2021.39245.

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Abstract: Within the biometric industry, computerized person identification using ear pictures is a hot topic. The ear, like other biometrics like the face, iris, and fingerprints, contains a huge number of particular and unique traits that may be used to identify a person. Due to the mask-wearing scenario, most face detection methods fail in this present international COVID-19 pandemic. The eardrum is a great data source for inactive person authentication since it doesn't necessitate the person we're attempting to pinpoint to cooperate, and the structure of the ear doesn't change significantl

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A, Saritha Haridas, and Kesavan Namboothiri T. "Ear Biometrics and Face Mask Detection Using Haar Features." International Journal of Engineering Research in Electronics and Communication Engineering 9, no. 7 (2022): 5–10. http://dx.doi.org/10.36647/ijerece/09.07.a002.

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The world depends on systems to provide secure environments and services to the people. Biometrics authentication (or realistic authentication) is employed in applied science as a style of identification and access control. Biometric System for authentic identification of a private. My motto is to make a biometric system using the ear as the main object. It also has no changes as expression change The human ear are neither affected by expressions like faces are nor do need closer touching like finger-prints do, which is more useful in the situation where the protection mechanism is needed as l

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Rozprawy doktorskie na temat "Ear biometric"

Saleh, Mohamed Ibrahim. "Using Ears for Human Identification." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/33158.

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Biometrics includes the study of automatic methods for distinguishing human beings based on physical or behavioral traits. The problem of finding good biometric features and recognition methods has been researched extensively in recent years. Our research considers the use of ears as a biometric for human recognition. Researchers have not considered this biometric as much as others, which include fingerprints, irises, and faces. This thesis presents a novel approach to recognize individuals based on their outer ear images through spatial segmentation. This approach to recognizing is also good

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BARRA, SILVIO. "Design of a Multi-biometric Platform, based on physical traits and physiological measures: Face, Iris, Ear, ECG and EEG." Doctoral thesis, Università degli Studi di Cagliari, 2016. http://hdl.handle.net/11584/266893.

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Security and safety is one the main concerns both for governments and for private companies in the last years so raising growing interests and investments in the area of biometric recognition and video surveillance, especially after the sad happenings of September 2001. Outlays assessments of the U.S. government for the years 2001-2005 estimate that the homeland security spending climbed from $56.0 billions of dollars in 2001 to almost $100 billion of 2005. In this lapse of time, new pattern recognition techniques have been developed and, even more important, new biometric traits have b

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Hurley, David J. "Force field feature extraction for ear biometrics." Thesis, University of Southampton, 2001. https://eprints.soton.ac.uk/256792/.

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The overall objective in defining feature space is to reduce the dimensionality of the original pattern space, whilst maintaining discriminatory power for classification. To meet this objective in the context of ear biometrics a novel force field transformation is introduced in which the image is treated as an array of mutually attracting particles that act as the source of a Gaussian force field. In a similar way to Newton’s Law of Universal Gravitation pixels are imagined to attract each other according to the product of their intensities and inversely to the square of the distance between t

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Arbab-Zavar, Banafshe. "On guided model-based analysis for ear biometrics." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/72062/.

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Ears are a new biometric with major advantage in that they appear to maintain their structure with increasing age. Current approaches have exploited 2D and 3D images of the ear in human identification. Contending that the ear is mainly a planar shape we use 2D images, which are consistent with deployment in surveillance and other planar-image scenarios. So far ear biometric approaches have mostly used general properties and overall appearance of ear images in recognition, while the structure of the ear has not been discussed. In this thesis, we propose a new model-based approach to ear biometr

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Ibrahim, Mina Ibrahim Samaan. "Wavelet based approaches for detection and recognition in ear biometrics." Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/340675/.

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One of the most recent trends in biometrics is recognition by ear appearance in head profile images. Ear localization to determine the region of interest containing ears is an important step in an ear biometric system. To this end, we propose a robust, simple and effective method for ear detection from profile images by employing a bank of curved and stretched Gabor wavelets, known as banana wavelets. Our analysis shows that the banana wavelets demonstrate better performance than Gabor wavelets technique for ear localization. This indicates that the curved wavelets are advantageous for the det

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Cadavid, Steven. "Human Identification Based on Three-Dimensional Ear and Face Models." Scholarly Repository, 2011. http://scholarlyrepository.miami.edu/oa_dissertations/516.

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We propose three biometric systems for performing 1) Multi-modal Three-Dimensional (3D) ear + Two-Dimensional (2D) face recognition, 2) 3D face recognition, and 3) hybrid 3D ear recognition combining local and holistic features. For the 3D ear component of the multi-modal system, uncalibrated video sequences are utilized to recover the 3D ear structure of each subject within a database. For a given subject, a series of frames is extracted from a video sequence and the Region-of-Interest (ROI) in each frame is independently reconstructed in 3D using Shape from Shading (SFS). A ﬁdelity measure i

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Mursalin, Md. "Human identification using ear biometrics: A complete pipeline from detection to recognition." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2023. https://ro.ecu.edu.au/theses/2622.

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Biometrics is a critical component of cybersecurity that identifies persons by verifying their behavioral and physical traits. It is the most precise and powerful physical security solution for identity verification presently in use. In biometric-based authentication, each individual can be correctly recognized based on their intrinsic behavioral or physical features. The human ear is an important biometric with highly discriminating features such that even identical twins have different ear shapes. Owing to the easy acquisition, invariance to expression, and stable structure over a long perio

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Kaloorazi, Maboud Farzaneh. "3D Ear Recognition Based on Force Field Transform." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4891.

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Reducing the dimensionality of the original pattern space in a definition of feature space while maintaining discriminatory power for classification is a general goal in pattern recognition. To accomplish this goal in the area of ear biometrics a highly recognized work was proposed by D. Hurley in 2D space. We were inspired by his work and developed a new method for 3D data. In a different way to Hurley’s work we obtain a potential energy surface from 3D depth image which underlies the force field and associated vector field has its own characteristics. Our feature extraction is conducted by c

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Hansley, Earnest Eugene. "Identification of Individuals from Ears in Real World Conditions." Scholar Commons, 2018. http://scholarcommons.usf.edu/etd/7162.

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A number of researchers have shown that ear recognition is a viable alternative to more common biometrics such as fingerprint, face and iris because the ear is relatively stable over time, the ear is non-invasive to capture, the ear is expressionless, and both the ear’s geometry and shape have significant variation among individuals. Researchers have used different approaches to enhance ear recognition. Some researchers have improved upon existing algorithms, some have developed algorithms from scratch to assist with recognizing individuals by ears, and some researchers have taken algorithms t

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Canto, Moreira Nuno. "MRI Studies of the Fetal Brain and Cranium." Doctoral thesis, Uppsala universitet, Enheten för radiologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-164685.

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Ultrasound is the primary modality for fetal imaging, but Magnetic Resonance Imaging nowadays has a valuable complementary role as it often reveals findings that alter pregnancy management. Knowledge on some clinically relevant areas of the normal fetal development is still lacking, and this was the aim of this project. We wanted 1) to obtain reference MRI data of normal brain measurements before 24 gestation weeks (GW), 2) to study the development of the hippocampus, 3) to study the development of the ear and 4) to test the ability of MRI for evaluating the lip and palate. For this, we retros

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Książki na temat "Ear biometric"

Prakash, Surya, and Phalguni Gupta. Ear Biometrics in 2D and 3D. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-375-0.

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Chen, Hui, and Bir Bhanu. Human Ear Recognition by Computer. Springer, 2008.

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Chen, Hui, and Bir Bhanu. Human Ear Recognition by Computer. Springer London, Limited, 2010.

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Gupta, Phalguni, and Surya Prakash. Ear Biometrics in 2D And 3D: Localization and Recognition. Springer London, Limited, 2015.

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Gupta, Phalguni, and Surya Prakash. Ear Biometrics in 2D and 3D: Localization and Recognition. Springer, 2015.

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Gupta, Phalguni, and Surya Prakash. Ear Biometrics in 2D and 3D: Localization and Recognition. Ingramcontent, 2016.

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Części książek na temat "Ear biometric"

Derawi, Mohammad, Patrick Bours, and Ray Chen. "Biometric Acoustic Ear Recognition." In Signal Processing for Security Technologies. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47301-7_4.

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Wu, Jiangwei, Zhichun Mu, and Kai Wang. "3D Pure Ear Extraction and Recognition." In Biometric Recognition. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35136-5_27.

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Cantoni, Virginio, Dimo T. Dimov, and Atanas Nikolov. "3D Ear Analysis by an EGI Representation." In Biometric Authentication. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13386-7_11.

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Yuan, Li, Haonan Zhao, Yi Zhang, and Zeyu Wu. "Ear Alignment Based on Convolutional Neural Network." In Biometric Recognition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97909-0_60.

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Yuan, Li, Zhi-Chun Mu, and Fan Yang. "A Review of Recent Advances in Ear Recognition." In Biometric Recognition. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25449-9_32.

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Bhanu, Bir. "Ear Shape for Biometric Identification." In Encyclopedia of Cryptography and Security. Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-5906-5_738.

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Guo, Mingduo, Zhichun Mu, and Li Yuan. "Multimodal Ear Recognition Based on 2D+3D Feature Fusion." In Biometric Recognition. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35136-5_28.

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Luo, Shuang, Zhichun Mu, and Baoqing Zhang. "Discriminative Super-Resolution Method for Low-Resolution Ear Recognition." In Biometric Recognition. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12484-1_50.

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Zhu, Qinping, and Zhichun Mu. "An Efficient 3D Ear Recognition System Based on Indexing." In Biometric Recognition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97909-0_54.

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Yuan, Li, Xiaoyu Wang, and Zhichun Mu. "Auricular Point Localization Oriented Region Segmentation for Human Ear." In Biometric Recognition. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86608-2_8.

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Streszczenia konferencji na temat "Ear biometric"

Chutani, Parul, and Nitin Sharma. "Ear Recognition: Advancements and Trends in Biometric Identification." In 2024 International Conference on Expert Clouds and Applications (ICOECA). IEEE, 2024. http://dx.doi.org/10.1109/icoeca62351.2024.00168.

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Uchida, Hironori, Jo Kawabata, Takeshi Hamasaki, Yujie Li, and Yoshihisa Nakatoh. "Improving Biometric Accuracy Using Ear Canal Images: Removing Overexposed and Underexposed Images Through a Lightweight Algorithm." In 2024 IEEE International Symposium on Consumer Technology (ISCT). IEEE, 2024. https://doi.org/10.1109/isct62336.2024.10791102.

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Boujnah, Sana, Radhia Ferjaoui, and Anouar Ben Khalifa. "A Novel Deep Neural Network Model using Resnet50-BiLSTM for Biometric Ear Recognition in Degraded Conditions." In 2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP). IEEE, 2024. http://dx.doi.org/10.1109/atsip62566.2024.10638889.

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Alomari, Eman Abdullah M., Su Yang, Sanaul Hoque, and Farzin Deravi. "Ear-based Person Recognition using Pix2Pix GAN Augmentation." In 2024 International Conference of the Biometrics Special Interest Group (BIOSIG). IEEE, 2024. https://doi.org/10.1109/biosig61931.2024.10786744.

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Mohamed, Youssef, Zeyad Youssef, Ahmed Heakl, and Ahmed B. Zaky. "Advancing Ear Biometrics: Enhancing Accuracy and Robustness Through Deep Learning." In 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652851.

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Derawi, Mohammad. "Biometric acoustic ear recognition." In 2016 International Conference on Bio-engineering for Smart Technologies (BioSMART). IEEE, 2016. http://dx.doi.org/10.1109/biosmart.2016.7835597.

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Yazdanpanah, Ali Pour, Karim Faez, and Rasoul Amirfattahi. "Multimodal biometric system using face, ear and gait biometrics." In 2010 10th International Conference on Information Sciences, Signal Processing and their Applications (ISSPA). IEEE, 2010. http://dx.doi.org/10.1109/isspa.2010.5605477.

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López-Hernández, Eydi, Andrea Magadán-Salazar, and Raúl Pinto-Elías. "Survey of ear biometric systems." In 2022 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE). IEEE, 2022. http://dx.doi.org/10.1109/icmeae58636.2022.00027.

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Emersic, Ziga, and Peter Peer. "Toolbox for ear biometric recognition evaluation." In IEEE EUROCON 2015 - International Conference on Computer as a Tool (EUROCON). IEEE, 2015. http://dx.doi.org/10.1109/eurocon.2015.7313758.

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G.S., Badrinath, and Phalguni Gupta. "Feature Level Fused Ear Biometric System." In 2009 Seventh International Conference on Advances in Pattern Recognition (ICAPR 2009). IEEE, 2009. http://dx.doi.org/10.1109/icapr.2009.27.

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