Ear Detection Based on Arc-Masking Extraction and AdaBoost Polling Verification

Shih, Hao-Chung; Ho, Chian C.; Chang, Hsuan; Wu, Chin-Song

doi:10.1109/iih-msp.2009.75

Cited by 10 publications

(8 citation statements)

References 11 publications

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“…Learning-based detection methods have been proposed owing to their accuracy and robustness advantages for ear detection, e.g., the AdaBoost algorithm and its modified version [24,25] and ear detection involving faster region-based convolutional neural network (Faster R-CNN) frameworks [26], a modified multiple scale faster region-based convolutional neural network [14], geometrics morphometrics and deep learning [27] and convolutional encoder-decoder networks for ear detection [28]. Automatic ear detection methods have achieved good performance under no occlusion or noise conditions; for example, the accuracy of the method in [12] is 93.34% for 700 images, and that in [19] is 97.57% for 267 images.…”

Section: Automatic 2d Ear Detectionmentioning

confidence: 99%

Local and Holistic Feature Fusion for Occlusion-Robust 3D Ear Recognition

Zhu

Zhang

2018

Symmetry

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Occlusion over ear surfaces results in performance degradation of ear registration and recognition systems. In this paper, we propose an occlusion-resistant three-dimensional (3D) ear recognition system consisting of four primary components: (1) an ear detection component, (2) a local feature extraction and matching component, (3) a holistic matching component, and (4) a decision-level fusion algorithm. The ear detection component is implemented based on faster region-based convolutional neural networks. In the local feature extraction and matching component, a symmetric space-centered 3D shape descriptor based on the surface patch histogram of indexed shapes (SPHIS) is used to generate a set of keypoints and a feature vector for each keypoint. Then, a two-step noncooperative game theory (NGT)-based method is proposed. The proposed symmetric game-based method is effectively applied to determine a set of keypoints that satisfy the rigid constraints from initial keypoint correspondences. In the holistic matching component, a proposed variant of breed surface voxelization is used to calculate the holistic registration error. Finally, the decision-level fusion algorithm is applied to generate the final match scores. Evaluation results from experiments conducted show that the proposed method produces competitive results for partial occlusion on a dataset consisting of natural and random occlusion.

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Section: Automatic 2d Ear Detectionmentioning

confidence: 99%

Local and Holistic Feature Fusion for Occlusion-Robust 3D Ear Recognition

Zhu

Zhang

2018

Symmetry

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“…Abaza et al [31] modified the Adaboost algorithm and reduced the training time significantly. Shih et al [32] presented a two-step ear detection system, which utilized arc-masking candidate extraction and AdaBoost polling verification. Firstly, the ear candidates were extracted by an arc-masking edge search algorithm; then the ear was located by rough AdaBoost polling verification.…”

Section: Ear Detectionmentioning

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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“…Shih et.al. [5] also adopt Haar-like features and AdaBoost algorithm for their segmentation but they implemented Arc-masking edge search and multilayer mosaic to enhance the ear region.…”

Section: Ear Recognition Processmentioning

confidence: 99%

“…Shih et.al. [5] used the pixel intensities to highlight the ear region. Yuan and Mu, on the other hand, applied Active Shape Model which is based on the shape feature to extract the ear region in [6].…”

Section: Ear Recognition Processmentioning

confidence: 99%

Cross-band ear recognition in low or variant illumination environments

Ariffin

Jamil

2014

2014 International Symposium on Biometrics and Security Technologies (ISBAST)

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Ear biometric is slowly gaining its position in biometric studies. Just like fingerprint and iris, the ears are unique and have other advantages over current regular biometric methods. Besides those advantages, there are some issues arising for ear recognition. One of those is regarding the illumination. Low illumination may result in low quality image acquired resulting in low recognition rate. Based on this situation, we proposed a cross-band ear recognition to overcome the variant illumination problem. This method starts by measuring the environments illumination which will determine which type of images (i.e.: thermal or visible) acquired to be processed. Once determined, the images will undergo pre-processing before the ear region is being localized using Viola-Jones approach with Haar-like feature. The ear features will be extracted using local binary patterns operator. Euclidean distance of the feature of test image and database images will be calculated. The lowest Euclidean value will determine the individual identity (intra-and inter-variance).

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Ear Detection Based on Arc-Masking Extraction and AdaBoost Polling Verification

Cited by 10 publications

References 11 publications

Local and Holistic Feature Fusion for Occlusion-Robust 3D Ear Recognition

Local and Holistic Feature Fusion for Occlusion-Robust 3D Ear Recognition

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

Cross-band ear recognition in low or variant illumination environments

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