A survey on ear biometrics

Abaza, Ayman; Ross, Arun; Hebert, Christina L.; Harrison, Mary Ann F.; Nixon, Mark S.

doi:10.1145/2431211.2431221

Cited by 209 publications

(109 citation statements)

References 102 publications

(111 reference statements)

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“…Main advantage of using ear as biometric trait is it does not suffer from any changes when facial expression changes. Abaza et al (2013) presented a systematic discussion which includes available databases, detection and feature extraction techniques, as well as a survey of some unsolved ear recognition problems. An American police officer (Iannarelli, 1989), proposed a first nonautomated ear recognition system based on a set of 12 measurements.…”

Section: Related Work and Motivationmentioning

confidence: 99%

An Innovative Approach for Person Identification by Detection and Extraction of Optic Disc from Retina and Concha from Ear

Malathy

Sadiq²,

Annapurani

2015

Journal of Computer Science

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Abstract:Person identification based on unimodal biometric system suffers from noise, intra class similarity, non universality, distinctiveness and spoof attacks. To alleviate the problem faced in unimodal biometric system biometric traits are combined in multimodal biometric system. In this study a new approach, to improve the recognition rate, reduces computational complexity and storage space is presented. Distinct method of person identification using detection and extraction of optic disc from retina and concha from ear is carried out Region Of Interest (ROI) locator which is proposed here automatically detects the optic disc either from right or left eye and extracts it. Feature level fusion of optic disc and concha is done for recognition of a person. The method is tested with ROI locator and without ROI locator on publicly available databases and the experimental result shows that our multimodal biometric system outperforms with ROI locator than Without ROI locator. Matching Rate (MR) of 95 to 100% and Equal Error Rate (EER) of less than 10% is achieved with this system. The new approach was tested for unimodal system with ROI locator and was able to achieve 100% Matching Rate.

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Section: Related Work and Motivationmentioning

confidence: 99%

An Innovative Approach for Person Identification by Detection and Extraction of Optic Disc from Retina and Concha from Ear

Malathy

Sadiq²,

Annapurani

2015

Journal of Computer Science

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“…Abaza et al [17] contributed an excellent survey on ear recognition in March 2010. Their work covers the history of ear biometrics, a selection of available databases and a review of 2D and 3D ear recognition systems.…”

Section: Figure 1: Characteristics Of the Human Ear The German Criminmentioning

confidence: 99%

“…By fusing color and curvature information, the detection rate could be raised to 99.3% on the UCR dataset and 87.71% on UND collection F and a subset of collection G. The UCR dataset is not publicly available and is hence not covered in Section 2. For a description of this dataset see [17]. Another example for ear detection using contour lines of the ear is described by Attrachi et al [29].…”

Section: Ear Detectionmentioning

confidence: 99%

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Ear biometrics: a survey of detection, feature extraction and recognition methods

2012

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The possibility of identifying people by the shape of their outer ear was first discovered by the French criminologist Bertillon, and refined by the American police officer Iannarelli, who proposed a first ear recognition system based on only seven features.The detailed structure of the ear is not only unique, but also permanent, as the appearance of the ear does not change over the course of a human life. Additionally, the acquisition of ear images does not necessarily require a person's cooperation but is nevertheless considered to be non-intrusive by most people.Because of these qualities, the interest in ear recognition systems has grown significantly in recent years. In this survey, we categorize and summarize approaches to ear detection and recognition in 2D and 3D images. Then, we provide an outlook over possible future research in the field of ear recognition, in the context of smart surveillance and forensic image analysis, which we consider to be the most important application of ear recognition characteristic in the near future.

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“…Totally based on literature, feature extraction is done using various approaches: geometrical and global approach [17].…”

Section: Introductionmentioning

confidence: 99%

An Ear Recognition Method Based on Rotation Invariant Transformed DCT

Hourali

Gharravi

2017

IJECE

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Human recognition systems have gained great importance recently in a wide range of applications like access, control, criminal investigation and border security. Ear is an emerging biometric which has rich and stable structure and can potentially be implemented reliably and cost efficiently. Thus human ear recognition has been researched widely and made greatly progress. High recognition rates which are reported in most existing methods can be reached only under closely controlled conditions. Actually a slight amount of rotation and translation which is inescapable would be injurious for system performance. In this paper, a method that uses a transformed type of DCT is implemented to extract meaningful features from ear images. This algorithm is quite robust to ear rotation, translation and illumination. The proposed method is experimented on two popular databases, i.e. USTB II and IIT Delhi II, which achieves significant improvement in the performance in comparison to other methods with good efficiency based on LBP, DSIFT and Gabor. Also because of considering only important coefficients, this method is faster compared to other methods.

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A survey on ear biometrics

Cited by 209 publications

References 102 publications

An Innovative Approach for Person Identification by Detection and Extraction of Optic Disc from Retina and Concha from Ear

An Innovative Approach for Person Identification by Detection and Extraction of Optic Disc from Retina and Concha from Ear

Ear biometrics: a survey of detection, feature extraction and recognition methods

An Ear Recognition Method Based on Rotation Invariant Transformed DCT

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