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Artykuły w czasopismach na temat „Ear biometric”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

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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Murli, Manohar Yadav*, Nigam Kriti, Srivastava Ankit, and Kumar Pradeep. "RECENT ADVANCEMENTS IN EAR BIOMETRICS: A REVIEW." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 4 (2016): 653–59. https://doi.org/10.5281/zenodo.49811.

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Ascertaining the identity of a person is quite an important aspect of Forensic Science. There are so many physiological features have been proved to be highly discriminating among individuals. Biometrics play a significant role in individualizing a person. Fingerprint, Palm print, Retina and Iris recognition are the most popular examples of it. Fingerprint and iris are generally considered to allow more accurate biometric recognition than the face, but the face is more easily used in surveillance scenarios where fingerprint and iris capture are not feasible. However, the face by itself is not

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Singh, Law Kumar, Munish Khanna, Shankar Thawkar, and Jagadeesh Gopal. "Robustness for Authentication of the Human Using Face, Ear, and Gait Multimodal Biometric System." International Journal of Information System Modeling and Design 12, no. 1 (2021): 39–72. http://dx.doi.org/10.4018/ijismd.2021010103.

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Biometrics is the science that deals with personal human physiological and behavioral characteristics such as fingerprints, handprints, iris, voice, face recognition, signature recognition, ear recognition, and gait recognition. Recognition using a single trait has several problems and multimodal biometrics system is one of the solutions. In this work, the novel and imperative biometric feature gait is fused with face and ear biometric features for authentication and to overcome problems of the unimodal biometric recognition system. The authors have also applied various normalization methods t

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Kaur, Veerpal, Devershi Pallavi Bhatt, Pradeep Kumar Tiwari, and Sumegh Tharewal. "Blockchain technology combined with the CNN and Hashing algorithms enabled the secure storage of 3D biometric face and ear data." Journal of Discrete Mathematical Sciences & Cryptography 26, no. 3 (2023): 729–38. http://dx.doi.org/10.47974/jdmsc-1745.

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The integration of biometrics and blockchain is proposed as a sophisticated and enduring security strategy to protect sensitive information in the digital era. 3D biometric data will not be affected by changes in posture and changes of illumination. This research proposed a security model for 3D multimodal (face and ear). To secure the 3D data uses the convolutional neural networks (CNNs) for 3D image feature extraction based on fusion of 3D face and ear data. Further compare the different hashing algorithms for the security of 3D multimodal Biometric model in Blockchain. This research also co

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Lalwani*, Natasha, Aishna Sharma, and Dr Mani Roja M. Edinburgh. "Biometric Identification using Human Ear." International Journal of Engineering and Advanced Technology 9, no. 1 (2019): 4893–98. http://dx.doi.org/10.35940/ijeat.a2027.109119.

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Biometrics refers to the metrics of the human characteristics which has gained much popularity in recent times. It is a form of identification and access control. Widely used forms of biometrics are facial recognition, finger print recognition, iris recognition, etc. but the drawback is that most of these features change over time. The human ear is a cogent source of data to classify biometrically since its attributes do not change substantially as time progresses. This paper explores the field of ear biometric wherein the database images are re-sized to 128 x 256 pixels and then converted to

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Lahkar, Rahul, and Khurshid Alam Borbora. "Automatic segmentation of human ear in the wild." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 1 (2024): 333. http://dx.doi.org/10.11591/ijeecs.v34.i1.pp333-341.

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Ear biometrics has been a challenging and distinctive research area in recent times. The human ear possesses unique promising attributes that are being used by the researchers to carry out significant improvements in the field of human recognition using ear as a biometric. In order to achieve efficiency on any ear biometric system, the detection and segmentation of the human ear need to be performed precisely. Feeding accurately segmented images to the recognition system will result in higher recognition accuracy. In this paper, we present our work of segmentation of human ears from the images

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Lahkar, Rahul, and Khurshid Alam Borbora. "Automatic segmentation of human ear in the wild." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 1 (2024): 333–41. https://doi.org/10.11591/ijeecs.v34.i1.pp333-341.

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Ear biometrics has been a challenging and distinctive research area in recent times. The human ear possesses unique promising attributes that are being used by the researchers to carry out significant improvements in the field of human recognition using ear as a biometric. In order to achieve efficiency on any ear biometric system, the detection and segmentation of the human ear need to be performed precisely. Feeding accurately segmented images to the recognition system will result in higher recognition accuracy. In this paper, we present our work of segmentation of human ears from the images

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Sulavko, Alexey Е., and Alexander А. Khramov. "Biometric authentication method based on cepstral characteristics of external ear echograms and biometrics-to-code neural converter." Journal Of Applied Informatics 17, no. 1 (97) (2022): 69–82. http://dx.doi.org/10.37791/2687-0649-2022-17-1-69-82.

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Open biometric images (fingerprint, iris, face) are "in sight" and therefore compromised in the natural environment. In this work, it is proposed to use data on the internal structure of the outer ear obtained using echography as biometric images. The individual characteristics of the ear canal of subjects are hidden from direct observation and cannot be copied by photographing. The proposed authentication method is based on cepstral analysis of echograms of the ear canal using neural network biometrics to code converters, trained in accordance with GOST R 52633.5. The neural network biometric

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Snehlata, Barde. "Multimodal biometrics system with face, ear, and foot fusion techniques." i-manager’s Journal on Pattern Recognition 10, no. 2 (2023): 27. http://dx.doi.org/10.26634/jpr.10.2.20352.

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Biometrics, as an identification method, is used for various applications, particularly in security technologies. The integration of multiple biometric sources aims to overcome limitations observed in unimodal systems, enhancing recognition accuracy. Fusion techniques, categorized into sensor level, feature level, matching score level, decision level, and rank level, are explored to optimize the combination of information from different modalities. Various fusion schemes, such as feature-level fusion, decision-level fusion, and hybrid systems, are investigated for their effectiveness in integr

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Jobi, Melon, Sajin Thomas K, Smrithi K H, Veena C V, and V. S. Anjali. "Ear Cavity Biometric Recognition." IOSR Journal of Biotechnology and Biochemistry 03, no. 02 (2017): 01–04. http://dx.doi.org/10.9790/264x-03020104.

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Gore, Dayanand Bharat. "Comparative Study on Feature Extractions for Ear Recognition." International Journal of Applied Evolutionary Computation 10, no. 2 (2019): 8–18. http://dx.doi.org/10.4018/ijaec.2019040102.

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Biometrics includes the study of automatic methods for distinguishing human beings based on physical or behavioural traits. The problem of finding good biometric features and recognition methods has been researched extensively in recent years. This research considers the use of ears as a biometric for human recognition. In this article, basic feature extraction techniques are implemented such as Harris Feature, FAST Feature extraction and SURF Feature Extraction. All the images are taken from standard database and each image has different angles because of any criminal investigation, accident,

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Ma, Yichao, Zengxi Huang, Xiaoming Wang, and Kai Huang. "An Overview of Multimodal Biometrics Using the Face and Ear." Mathematical Problems in Engineering 2020 (October 31, 2020): 1–17. http://dx.doi.org/10.1155/2020/6802905.

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In the recent years, we have witnessed the rapid development of face recognition, though it is still plagued by variations such as facial expressions, pose, and occlusion. In contrast to the face, the ear has a stable 3D structure and is nearly unaffected by aging and expression changes. Both the face and ear can be captured from a distance and in a nonintrusive manner, which makes them applicable to a wider range of application domains. Together with their physiological structure and location, the ear can readily serve as supplement to the face for biometric recognition. It has been a trend t

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Paul, Padma P., and Marina L. Gavrilova. "A Novel Cross Folding Algorithm for Multimodal Cancelable Biometrics." International Journal of Software Science and Computational Intelligence 4, no. 3 (2012): 20–37. http://dx.doi.org/10.4018/jssci.2012070102.

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Multimodal biometric systems have emerged as highly successful new approach to combat problems of unimodal biometric system such as intraclass variability, interclass similarity, data quality, non-universality, and sensitivity to noise. However, one major issue pertinent to unimodal system remains, which has to do with actual biometric characteristics of users being permanent and their number being limited. Thus, if a user’s biometric is compromised, it might be impossible or highly difficult to replace it in a particular system. The concept of cancelable biometric or cancelability is to trans

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Moolla, Yaseen, Anton De Kock, Gugulethu Mabuza-Hocquet, Cynthia Sthembile Ntshangase, Norman Nelufule, and Portia Khanyile. "Biometric Recognition of Infants using Fingerprint, Iris, and Ear Biometrics." IEEE Access 9 (2021): 38269–86. http://dx.doi.org/10.1109/access.2021.3062282.

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Chang, Zhuo, Lin Wang, Binbin Li, and Wenyuan Liu. "MetaEar: Imperceptible Acoustic Side Channel Continuous Authentication Based on ERTF." Electronics 11, no. 20 (2022): 3401. http://dx.doi.org/10.3390/electronics11203401.

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With the development of ubiquitous mobile devices, biometrics authentication has received much attention from researchers. For immersive experiences in AR (augmented reality), convenient continuous biometric authentication technologies are required to provide security for electronic assets and transactions through head-mounted devices. Existing fingerprint or face authentication methods are vulnerable to spoof attacks and replay attacks. In this paper, we propose MetaEar, which harnesses head-mounted devices to send FMCW (Frequency-Modulated Continuous Wave) ultrasonic signals for continuous b

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Yasuhara, Masaki, Isao Nambu, and Shohei Yano. "Bilateral Ear Acoustic Authentication: A Biometric Authentication System Using Both Ears and a Special Earphone." Applied Sciences 12, no. 6 (2022): 3167. http://dx.doi.org/10.3390/app12063167.

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In existing biometric authentication methods, the user must perform an authentication operation such as placing a finger in a scanner or facing a camera. With ear acoustic authentication, the acoustic characteristics of the ear canal can be used as biometric information. Therefore, a person wearing earphones does not need to perform any authentication operation. Existing studies which use the acoustic characteristics of the ear canal as biometric information only measure the characteristics of one ear. However, the acoustic characteristics of the human ear canal can be measured from both ears.

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Etter, Lauren, Alinani Simukanga, Wenda Qin, et al. "Project SEARCH (Scanning EARs for Child Health): validating an ear biometric tool for patient identification in Zambia." Gates Open Research 4 (November 6, 2020): 168. http://dx.doi.org/10.12688/gatesopenres.13197.1.

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Patient identification in low- to middle-income countries is one of the most pressing public health challenges of our day. Given the ubiquity of mobile phones, their use for health-care coupled with a biometric identification method, present a unique opportunity to address this challenge. Our research proposes an Android-based solution of an ear biometric tool for reliable identification. Unlike many popular biometric approaches (e.g., fingerprints, irises, facial recognition), ears are noninvasive and easily accessible on individuals across a lifespan. Our ear biometric tool uses a combinatio

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Jbara, Wurood A. "Ear biometric verification approach based on morphological and geometric invariants." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (2020): 1479–84. https://doi.org/10.11591/ijeecs.v20.i3.pp1479-1484.

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Biometric verification based on ear features is modern filed for scientific research. As known, there are many biometric identifiers that can identify people such as fingerprints, iris and speech. In this paper, the focus is placed on the ear biometric model in order to verifying the identity of persons. The main idea is based on used the moments as ear feature extractors. The proposed approach included some operations as follow: image capturing, edge detection, erosion, feature extraction, and matching. The proposed approach has been tested using many images of the ears with different states.

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Luu, Phuong Quang, Bay Van Nguyen, and Huy Quoc Nguyen. "Kinship verification via ear images: A comparative study." HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY 15, no. 1 (2025): 47–57. https://doi.org/10.46223/hcmcoujs.tech.en.15.1.3683.2025.

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Kinship verification is crucial in daily life, especially in the legal field. Nowadays, most kinship verification methods utilize the advantages of human DNA and facial features. However, these methods require a lot of complex procedures, so they are unsuitable for real-time application. Therefore, researchers started to propose other promising biometrics, and the human ear is one of the most potential. The human ear has long been recognized as a robust biometric trait, comparable to others, such as face, iris, and fingerprint. This paper proposes using ear images to identify human kinship bas

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Salman, Duha D., Raghad A. Azeez, and Adul mohssen J. Hossen. "Key Generation from Multibiometric System Using Meerkat Algorithm." Engineering and Technology Journal 38, no. 3B (2020): 115–27. http://dx.doi.org/10.30684/etj.v38i3b.652.

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Biometrics are short of revocability and privacy while cryptography cannot adjust the user’s identity. By obtaining cryptographic keys using biometrics, one can obtain the features such as revocability, assurance about user’s identity, and privacy. Multi-biometrical based cryptographic key generation approach has been proposed, subsequently, left and right eye and ear of a person are uncorrelated from one to other, and they are treated as two independent biometrics and combine them in our system. None-the-less, the encryption keys are produced with the use of an approach of swarm intelligence.

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Salman, Duha D., Raghad A. Azeez, and Adul mohssen J. Hossen. "Key Generation from Multibiometric System Using Meerkat Algorithm." Engineering and Technology Journal 38, no. 3B (2020): 115–27. http://dx.doi.org/10.30684/etj.v38i3b.652.

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Biometrics are short of revocability and privacy while cryptography cannot adjust the user’s identity. By obtaining cryptographic keys using biometrics, one can obtain the features such as revocability, assurance about user’s identity, and privacy. Multi-biometrical based cryptographic key generation approach has been proposed, subsequently, left and right eye and ear of a person are uncorrelated from one to other, and they are treated as two independent biometrics and combine them in our system. None-the-less, the encryption keys are produced with the use of an approach of swarm intelligence.

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M., Maitreyee. "Pinna of ear: a potential biometric identifier." International Journal of Research in Medical Sciences 5, no. 9 (2017): 3843. http://dx.doi.org/10.18203/2320-6012.ijrms20173640.

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Background: Individual differences in morphometric and somatoscopic observations of pinna of ear were studied to explore the potential of pinna of ear as a biometric tool.Methods: Morphometric and somatoscopic data of right ear of 350 Indian individuals (Age: 17-25 years) was collected. Measurements of pinna length, pinna width, pinna root, pinna projection, intertragic distance, ear lobe length, and ear lobe width were taken. Observations were done for presence or absence of Darwin tubercle, flat or rolled helix, and attached or free ear lobe. Probability statistics was extrapolated to assess

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Tharewal, Sumegh, Timothy Malche, Pradeep Kumar Tiwari, et al. "Score-Level Fusion of 3D Face and 3D Ear for Multimodal Biometric Human Recognition." Computational Intelligence and Neuroscience 2022 (April 14, 2022): 1–9. http://dx.doi.org/10.1155/2022/3019194.

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A novel multimodal biometric system is proposed using three-dimensional (3D) face and ear for human recognition. The proposed model overcomes the drawbacks of unimodal biometric systems and solves the 2D biometric problems such as occlusion and illumination. In the proposed model, initially, the principal component analysis (PCA) is utilized for 3D face recognition. Thereafter, the iterative closest point (ICP) is utilized for 3D ear recognition. Finally, the 3D face is fused with a 3D ear using score-level fusion. The simulations are performed on the Face Recognition Grand Challenge database

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A. Jbara, Wurood. "Ear biometric verification approach based on morphological and geometric invariants." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (2020): 1479. http://dx.doi.org/10.11591/ijeecs.v20.i3.pp1479-1484.

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<p>Biometric verification based on ear features is modern filed for scientific research. As known, there are many biometric identifiers that can identify people such as fingerprints, iris and speech. In this paper, the focus is placed on the ear biometric model in order to verifying the identity of persons. The main idea is based on used the moments as ear feature extractors. The proposed approach included some operations as follow: image capturing, edge detection, erosion, feature extraction, and matching. The proposed approach has been tested using many images of the ears with differen

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Abed alkareem hussain Ayash, hawraa, and hawraa hassan abbas. "a survey on multi-biometric fusion approaches." Kerbala Journal for Engineering Sciences 3, no. 2 (2023): 79–100. https://doi.org/10.63463/kjes1074.

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The goal of biometrics is to reliably and robustly identify people based on their unique personal characteristics, primarily for security and authentication needs, but also to identify and track the users of more intelligent applications. Fingerprints, iris, palm print, face and voices are frequently used modalities, but there are numerous more potential biometrics, such as stride, ear image, retina, DNA, and even behavior. As an automatic way to identify persons depend on just one (single modal biometrics) or a mix of (multi-modal biometrics). A fusion of two or more photos can be utilized to

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Lavanya, B., Ahmad Taher Azar, and H. Hannah Inbarani. "Neighborhood Rough Set Approach With Biometric Application." International Journal of Sociotechnology and Knowledge Development 14, no. 1 (2022): 1–20. http://dx.doi.org/10.4018/ijskd.289041.

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This paper provides a new approach for human identification based on Neighborhood Rough Set (NRS) algorithm with biometric application of ear recognition. The traditional rough set model can just be used to evaluate categorical features. The neighborhood model is used to evaluate both numerical and categorical features by assigning different thresholds for different classes of features. The feature vectors are obtained from ear image and ear matching process is performed. Actually, matching is a process of ear identification. The extracted features are matched with classes of ear images enroll

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Fukuda, Ryuhi, Yuto Yokoyanagi, Chotirose Prathom, and Yoshifumi Okada. "Toward Personal Identification Using Multi-Angle-Captured Ear Images: A Feasibility Study." Applied Sciences 15, no. 6 (2025): 3329. https://doi.org/10.3390/app15063329.

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The ear is an effective biometric feature for personal identification. Although numerous studies have attempted personal identification using frontal-view images of the ear, only a few have attempted personal identification using multi-angle-captured ear images. To expand the extant literature and facilitate future biometric authentication technologies, we explore the feasibility of personal identification using multidirectionally captured ear images and attempted to identify the direction-independent feature points that contribute to the identification process. First, we construct a convoluti

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Herbadji, Abderrahmane, Zahid Akhtar, Kamran Siddique, et al. "Combining Multiple Biometric Traits Using Asymmetric Aggregation Operators for Improved Person Recognition." Symmetry 12, no. 3 (2020): 444. http://dx.doi.org/10.3390/sym12030444.

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Biometrics is a scientific technology to recognize a person using their physical, behavior or chemical attributes. Biometrics is nowadays widely being used in several daily applications ranging from smart device user authentication to border crossing. A system that uses a single source of biometric information (e.g., single fingerprint) to recognize people is known as unimodal or unibiometrics system. Whereas, the system that consolidates data from multiple biometric sources of information (e.g., face and fingerprint) is called multimodal or multibiometrics system. Multibiometrics systems can

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Paul, Padma P., and Marina L. Gavrilova. "Cancelable Fusion of Face and Ear for Secure Multi-Biometric Template." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 3 (2013): 80–94. http://dx.doi.org/10.4018/ijcini.2013070105.

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Biometric fusion to achieve multimodality has emerged as a highly successful new approach to combat problems of unimodal biometric system such as intraclass variability, interclass similarity, data quality, non-universality, and sensitivity to noise. The authors have proposed new type of biometric fusion called cancelable fusion. The idea behind the cancelable biometric or cancelability is to transform a biometric data or feature into a new one so that the stored biometric template can be easily changed in a biometric security system. Cancelable fusion does the fusion of multiple biometric tra

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Goel, Sajal Kumar, and Mrudula Meduri. "Ear Biometric System using GLCM Algorithm." International Journal of Information Technology and Computer Science 9, no. 10 (2017): 68–76. http://dx.doi.org/10.5815/ijitcs.2017.10.07.

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Yan, Ping, and Kevin W. Bowyer. "Biometric Recognition Using 3D Ear Shape." IEEE Transactions on Pattern Analysis and Machine Intelligence 29, no. 8 (2007): 1297–308. http://dx.doi.org/10.1109/tpami.2007.1067.

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Amerson, Ellie A., Harrison Moss, Suresh Kumar, and Terry D. Brandebourg. "Assessing the Use of Biometric Ear Tags as Body Temperature Monitoring Devices in Swine." Journal of Animal Science 99, Supplement_2 (2021): 45–46. http://dx.doi.org/10.1093/jas/skab096.084.

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Abstract It is difficult to detect the subtle changes associated with sickness behaviors in individual pigs early enough to prevent disease outbreaks in group housing settings within large production facilities. This failure results in significant losses to the swine industry. Strategies that allow early detection of parameters such as febrile responses could therefore significantly improve herd health and producer profitability. Our objective was to determine if the use of a biometric ear tag capable of measuring temperature could be used to accurately monitor body temperature in swine. To ac

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Wang, Zi, Sheng Tan, Linghan Zhang, Yili Ren, Zhi Wang, and Jie Yang. "EarDynamic." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 1 (2021): 1–27. http://dx.doi.org/10.1145/3448098.

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Biometric-based authentication is gaining increasing attention for wearables and mobile applications. Meanwhile, the growing adoption of sensors in wearables also provides opportunities to capture novel wearable biometrics. In this work, we propose EarDynamic, an ear canal deformation based user authentication using in-ear wearables. EarDynamic provides continuous and passive user authentication and is transparent to users. It leverages ear canal deformation that combines the unique static geometry and dynamic motions of the ear canal when the user is speaking for authentication. It utilizes a

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Mursalin, Md, Mohiuddin Ahmed, and Paul Haskell-Dowland. "Biometric Security: A Novel Ear Recognition Approach Using a 3D Morphable Ear Model." Sensors 22, no. 22 (2022): 8988. http://dx.doi.org/10.3390/s22228988.

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Biometrics is a critical component of cybersecurity that identifies persons by verifying their behavioral and physical traits. In biometric-based authentication, each individual can be correctly recognized based on their intrinsic behavioral or physical features, such as face, fingerprint, iris, and ears. This work proposes a novel approach for human identification using 3D ear images. Usually, in conventional methods, the probe image is registered with each gallery image using computational heavy registration algorithms, making it practically infeasible due to the time-consuming recognition p

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Jayaram, M. A., G. K. Prashanth, and Sachin C. Patil. "Inertia-Based Ear Biometrics: A Novel Approach." Journal of Intelligent Systems 25, no. 3 (2016): 401–16. http://dx.doi.org/10.1515/jisys-2015-0047.

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AbstractThe human ear has been deemed to be a source of data for person identification in recent years. Ear biometrics has distinct advantages, such as visibility from a distance and ease with which images could be captured. This paper elaborates on a novel approach to ear biometrics. We propose moment of inertia-based biometric for the ears in any random orientation. The features concerned are the moment of inertia about the major and minor axes, corresponding radii of gyration, and the planar surface area of the ear. The databases of the said features were collected through ear images of 600

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Resmi K. R., Shijo M. Joseph, Raju G., Debabrata Swain, Om Prakash Das, and Biswaranjan Acharya. "Ear Recognition Using Rank Level Fusion of Classifiers Outputs." International Journal of Online and Biomedical Engineering (iJOE) 19, no. 03 (2023): 127–43. http://dx.doi.org/10.3991/ijoe.v19i03.36831.

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An individual's authentication plays a vital role in our daily life. In the last decade, biometric-based authentication has become more prevalent than traditional approaches like passwords and pins. Ear recognition has gained attention in the biometric community in recent years. Researchers defined several features for the identification of a person from ear image. The features play a vital role in the success of classification models. This paper considers an ensemble of features for designing a new classification model. The features are assessed in isolation as well as through feature-level f

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Manasa, M.G, and S. Gayathri. "Power Minimization Architecture for Multimodal Biometric System using Cadence." International Journal of Emerging Science and Engineering (IJESE) 6, no. 9 (2020): 1–5. https://doi.org/10.35940/ijese.H2478.046920.

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Physiological or behavioral characteristics of a person being identified or verified using biometric systems. The preprocessing block has the fir filter in which enhanced energy-efficiency has been obtained by introducing the low power architectures within it. The implementation of low power architectures in the fir filter part will further provide the optimization in the various parameters such as power, area and timing. Therefore, this will help us to do the biometrics process faster and efficient. 

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Athawale, Uttara, and Manoj Gupta. "Survey on Recent Ear Biometric Recognition Techniques." International Journal of Computer Sciences and Engineering 6, no. 6 (2018): 1208–11. http://dx.doi.org/10.26438/ijcse/v6i6.12081211.

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Benzaoui, Amir, Abdenour Hadid, and Abdelhani Boukrouche. "Ear biometric recognition using local texture descriptors." Journal of Electronic Imaging 23, no. 5 (2014): 053008. http://dx.doi.org/10.1117/1.jei.23.5.053008.

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Nguyen Quoc, Huy, and Vinh Truong Hoang. "Real-Time Human Ear Detection Based on the Joint of Yolo and RetinaFace." Complexity 2021 (November 8, 2021): 1–11. http://dx.doi.org/10.1155/2021/7918165.

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Biometric traits gradually proved their importance in real-life applications, especially in identification field. Among the available biometric traits, the unique shape of the human ear has also received loads of attention from scientists through the years. Hence, numerous ear-based approaches have been proposed with promising performance. With these methods, plenty problems can be solve by the distinctiveness of ear features, such as recognizing human with mask or diagnose ear-related diseases. As a complete identification system requires an effective detector for real-time application, and t

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Gu, Datong, Minh Nguyen, and Weiqi Yan. "Cross Models for Twin Recognition." International Journal of Digital Crime and Forensics 8, no. 4 (2016): 26–36. http://dx.doi.org/10.4018/ijdcf.2016100103.

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Nowadays, Biometrics has become a popular tool in personal identification as it can use physiological or behavioral characteristics to identify individuals. Recent advances in information technology has increased the accuracy of biometric to another level, there is still a slew of problems existed, such as complex environment, aging and unique problems. Among many classes of identifications, recognizing twins is one of the most difficult tasks as they resemble each other. This affects the use of biometrics in general cases and raises potential risks of biometrics in access control. In this pap

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