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Ear biometrics
- BIOMETRICS: Personal Identification in a Networked Society
, 1998
"... A new class of biometrics based upon ear features is introduced for use in the development of passive identification systems. The viability of the proposed biometric is shown both theoretically in terms of the uniqueness and measurability over time of the ear, and in practice through the implemen ..."
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A new class of biometrics based upon ear features is introduced for use in the development of passive identification systems. The viability of the proposed biometric is shown both theoretically in terms of the uniqueness and measurability over time of the ear, and in practice through the implementation of a computer vision based system. Each subject's ear is modeled as an adjacency graph built from the Voronoi digram of its curve segments. We introduce a novel graph matching based algorithm for authentication which takes into account the erroneous curve segments which can occur due to changes (e.g., lighting, shadowing, and occlusion) in the ear image. This new class of biometrics is ideal for passive identification because the features are robust and can be reliably extracted from a distance.
ON EMERGING BIOMETRIC TECHNOLOGIES
"... Many body parts, personal characteristics and imaging methods have been suggested and used for biometrics ..."
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Many body parts, personal characteristics and imaging methods have been suggested and used for biometrics
c World Scientic Publishing Company ROBUST PROFILE VERIFICATION SCHEME BASED ON EAR REFERENCE COORDINATE SYSTEM
, 2004
"... In this paper, a novel prole verication scheme based on the ear, contour lines and feature points from head prole images is proposed. From the side image of a human head taken by a CCD camera, the contour line of the image is extracted by the simple lling method and morphological ltering. Based on t ..."
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In this paper, a novel prole verication scheme based on the ear, contour lines and feature points from head prole images is proposed. From the side image of a human head taken by a CCD camera, the contour line of the image is extracted by the simple lling method and morphological ltering. Based on the reference coordinate set by using ear images enrolled in the database a priori, the features of the contour line such as the tip of the nose, bottom of the nose, eye point and so on are extracted. The contour of the given image is divided into segments by utilizing the extracted feature points. The directional code of each segment is proposed and the length of the segment is computed for generating the feature vector. The verication of the given prole is performed by matching these feature vectors with those of the enrolled database. The experimental results show validity of the developed method for feature extraction and pro le verication.
Electronic Letters on Computer Vision and Image Analysis 5(3):84-95, 2005 Ear Biometrics Based on Geometrical Feature Extraction
, 2005
"... Biometrics identification methods proved to be very efficient, more natural and easy for users than traditional methods of human identification. In fact, only biometrics methods truly identify humans, not keys and cards they posses or passwords they should remember. The future of biometrics will sur ..."
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Biometrics identification methods proved to be very efficient, more natural and easy for users than traditional methods of human identification. In fact, only biometrics methods truly identify humans, not keys and cards they posses or passwords they should remember. The future of biometrics will surely lead to systems based on image analysis as the data acquisition is very simple and requires only cameras, scanners or sensors. More importantly such methods could be passive, which means that the user does not have to take active part in the whole process or, in fact, would not even know that the process of identification takes place. There are many possible data sources for human identification systems, but the physiological biometrics seem to have many advantages over methods based on human behaviour. The most interesting human anatomical parts for such passive, physiological biometrics systems based on images acquired from cameras are face and ear. Both of those methods contain large volume of unique features that allow to distinctively identify many users and will be surely implemented into efficient biometrics systems for many applications. The article introduces to ear biometrics and presents its advantages over face biometrics in passive human identification systems. Then the geometrical method of feature extraction from human ear images in order to perform human identification is presented.
Telecommunication Systems manuscript No. (will be inserted by the editor) An Efficient Ear Recognition Technique Invariant to Illumination and Pose
"... Abstract This paper presents an efficient ear recognition technique which derives benefits from the local features of the ear and attempt to handle the problems due to pose, poor contrast, change in illumination and lack of registration. It uses (1) three image enhancement techniques in parallel to ..."
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Abstract This paper presents an efficient ear recognition technique which derives benefits from the local features of the ear and attempt to handle the problems due to pose, poor contrast, change in illumination and lack of registration. It uses (1) three image enhancement techniques in parallel to neutralize the effect of poor contrast, noise and illumination, (2) a local feature extraction technique (SURF) on enhanced images to minimize the effect of pose variations and poor image registration. SURF feature extraction is carried out on enhanced images to obtain three sets of local features, one for each enhanced image. Three nearest neighbor classifiers are trained on these three sets of features. Matching scores generated by all three classifiers are fused for final decision. The technique has been evaluated on two public databases, namely IIT Kanpur ear database and University of Notre Dame ear database (Collections E). Experimental results confirm that the use of proposed fusion significantly improves the recognition accuracy.
A Survey on Ear Biometrics
"... Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non-contact biometrics, such as face recognition; second, it is the most promising ca ..."
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Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non-contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion, earprint forensics, ear symmetry, ear classification, and ear individuality. This article provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers.
Research Article Ear Recognition Based on Gabor Features and KFDA
"... which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We propose an ear recognition system based on 2D ear images which includes three stages: ear enrollment, feature extraction, and ear recognition. Ear enrollment includes ear de ..."
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which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We propose an ear recognition system based on 2D ear images which includes three stages: ear enrollment, feature extraction, and ear recognition. Ear enrollment includes ear detection and ear normalization. The ear detection approach based on improved Adaboost algorithm detects the ear part under complex background using two steps: offline cascaded classifier training and online ear detection. Then Active Shape Model is applied to segment the ear part and normalize all the ear images to the same size. For its eminent characteristics in spatial local feature extraction and orientation selection, Gabor filter based ear feature extraction is presented in this paper. Kernel Fisher Discriminant Analysis (KFDA) is then applied for dimension reduction of the high-dimensional Gabor features. Finally distance based classifier is applied for ear recognition. Experimental results of ear recognition on two datasets (USTB and UND datasets) and the performance of the ear authentication system show the feasibility and effectiveness of the proposed approach. 1.
Ear Recognition based on Forstner and SIFT
, 2013
"... Extraction and expression of features are critical to improving the recognition rate of ear image recognition. This paper proposes a new ear recognition method based on SIFT (Scale-invariant feature transform) and Forstner corner detection technology. Firstly, Forstner corner points and SIFT keypoin ..."
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Extraction and expression of features are critical to improving the recognition rate of ear image recognition. This paper proposes a new ear recognition method based on SIFT (Scale-invariant feature transform) and Forstner corner detection technology. Firstly, Forstner corner points and SIFT keypoints are detected respectively. Then taking Forstner corner into the SIFT algorithm to calculate their descriptor as the image feature vectors. Finally ear recognition based on these feature is carried out with Euclidean distance as similarity measurement. Abi-directional matching algorithmis utilized for improving recognition rate. Experiments on USTB database show that the recognition rate reaches more 94%. The Experimental results prove the effectiveness of the proposed method in term of recognition accuracy in comparison with previous methods. It is robust to rigid changes of ear image and provides a new approach to the research for ear recognition.
