A New Segmentation Approach for Ear Recognition

Attarchi, S.; Faez, Karim; Rafiei, A.

doi:10.1007/978-3-540-88458-3_93

Cited by 27 publications

(22 citation statements)

References 6 publications

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“…The enclosed region is then labeled as the ear region. In contrast to [29], Prakash and Gupta prove the feasibility of edge-based ear detection on full profile images, where they achieved a detection rate of 96.63% on a subset of the UND-J2 collection. In [32] propose the same edge connectivity for ear recognition on 3D images.…”

Section: Ear Detectionmentioning

confidence: 97%

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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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Section: Ear Detectionmentioning

confidence: 97%

“…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]. They locate the outer contour of the ear by searching for the longest connected edge in the edge image.…”

Section: Ear Detectionmentioning

confidence: 99%

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

2012

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“…Nonetheless, it only works when the images only include facial parts, or else the other skin area may lead to an incorrect ear localization result. Attarchi et al [27] proposed an ear detection method based on the edge map and the mean ear template. The canny edge detector was used to obtain the edges of the ear image.…”

Section: Ear Detectionmentioning

confidence: 99%

“…Detection Approach Dataset Size Accuracy Rate % Burge and Burger [10] deformable contours N/A N/A Hurley et al [11] force filed transform N/A N/A Alvarez et al [12] the snake model and ovoid model N/A N/A Ansari and Gupta [13] Edge Detection and Curvature Estimation 700 93.34 Yuan and Mu [14] skin-color model and intensity contour information Video N/A Arbab [15] Hough Transform 942 91 Cummings et al [16] the image ray transform 252 98.4 Prakash and Gupta [17] skin color and Graph matching 1780 96.63 Yan and Bowyer [18] two-line landmark N/A N/A Yan and Bowyer [19] ear pit detection and active contour 415 78.8/85.54 Deepak et al [20] Active contour model 700 76.43 Chen and Bbanu [21] the step edge magnitude N/A N/A Chen and Bbanu [22] the step edge and ear shape model 312 92.6 Chen and Bbanu [23] skin classifier, the step edge and ear shape model 700 87.71 Ganesh et al [24] EBPSO and DTCWT 240 92.92 Sana et al [25] ear templates of different sizes N/A N/A Prakash et al [26] skin-color classifier and template matching 150 94 Attarchi et al [27] the edge map and the mean ear template 308 98.05 Halawani [28] predefined binary ear template 212 96.2 Joshi [29] oval shape detection 798 94% Islam et al [30] cascaded AdaBoost 203 100 Abaza et al [31] modified AdaBoost >2000 95 Yuan and Mu [33] improved AdaBoost 434 95.8…”

Section: Publicationsmentioning

confidence: 99%

Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks

Zhang

2017

Symmetry

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Abstract:Ear detection is an important step in ear recognition approaches. Most existing ear detection techniques are based on manually designing features or shallow learning algorithms. However, researchers found that the pose variation, occlusion, and imaging conditions provide a great challenge to the traditional ear detection methods under uncontrolled conditions. This paper proposes an efficient technique involving Multiple Scale Faster Region-based Convolutional Neural Networks (Faster R-CNN) to detect ears from 2D profile images in natural images automatically. Firstly, three regions of different scales are detected to infer the information about the ear location context within the image. Then an ear region filtering approach is proposed to extract the correct ear region and eliminate the false positives automatically. In an experiment with a test set of 200 web images (with variable photographic conditions), 98% of ears were accurately detected. Experiments were likewise conducted on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2) and University of Beira Interior Ear dataset (UBEAR), which contain large occlusion, scale, and pose variations. Detection rates of 100% and 98.22%, respectively, demonstrate the effectiveness of the proposed approach.

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“…Yan and Bowyer [43] have proposed a multistage process for enrollment that uses both 2D and 3D data and curvature estimation to detect the ear pit which is then used to initialize an elliptical active contour to locate and crop the 3D ear data. Attarchi et al [6] locate the outer contour of the ear by searching for the longest connected edge in the edge image. Hoogstrate et al [21] and Pun and Moon [35] have made significant progress in using ear detection techniques to identify targets in surveillance camera footage.…”

Section: Introductionmentioning

confidence: 99%

Entropy-cum-Hough-transform-based ear detection using ellipsoid particle swarm optimization

Prajwal

Srinivas

Manikantan

et al. 2015

Machine Vision and Applications

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Ear detection in facial images under varying pose, background and occlusion is a challenging issue. This paper proposes an entropy-cum-Hough-transform-based approach for enhancing the performance of an ear detection system, employing the unique combination of hybrid ear localizer (HEL) and ellipsoid ear classifier (EEC). By exploiting the entropic properties of the ear, as well as its ellipsoid structure, the HEL identifies the most probable location of the ear. To curb false ear acceptances by the HEL, the EEC verifies the actual presence of an ear in facial images, by attempting to fit an ellipse on the localized ear. EEC performs this by using the ellipsoid parametric set optimized by the evolutionary ellipsoid particle swarm optimization technique. The amount of success met by the EEC in doing so, i.e., the 'goodness of fit', is used to verify the presence of an ear. Experiments have been carried out on five benchmark face databases, namely Color FERET, Pointing Head Pose, UMIST, CMU-PIE, and FEI. The results show the efficiency and robustness of both HEL and EEC working individually and as a system, for enhanced ear detection.

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A New Segmentation Approach for Ear Recognition

Cited by 27 publications

References 6 publications

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

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

Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks

Entropy-cum-Hough-transform-based ear detection using ellipsoid particle swarm optimization

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