On Applicability of Tunable Filter Bank Based Feature for Ear Biometrics: A Study from Constrained to Unconstrained

Chowdhury, Debbrota Paul; Bakshi, Sambit; Guo, Guodong; Kumar, Pankaj

doi:10.1007/s10916-017-0855-8

Cited by 34 publications

(11 citation statements)

References 94 publications

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“…The ear recognition field has witnessed an increasing interest during the last few years and nearly perfect recognition rates have been attained under constrained conditions [24], [25], [26], [27]. The proposed techniques were developed and evaluated using small ear datasets gathered under laboratorylike settings with limited variations in lighting, head poses and occlusions.…”

Section: Related Workmentioning

confidence: 99%

Deep Convolutional Neural Networks for Unconstrained Ear Recognition

et al. 2020

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This paper employs state-of-the-art Deep Convolutional Neural Networks (CNNs), namely AlexNet, VGGNet, Inception, ResNet and ResNeXt in a first experimental study of ear recognition on the unconstrained EarVN1.0 dataset. As the dataset size is still insufficient to train deep CNNs from scratch, we utilize transfer learning and propose different domain adaptation strategies. The experiments show that our networks, which are fine-tuned using custom-sized inputs determined specifically for each CNN architecture, obtain state-of-the-art recognition performance where a single ResNeXt101 model achieves a rank-1 recognition accuracy of 93.45%. Moreover, we achieve the best rank-1 recognition accuracy of 95.85% using an ensemble of fine-tuned ResNeXt101 models. In order to explain the performance differences between models and make our results more interpretable, we employ the t-SNE algorithm to explore and visualize the learned features. Feature visualizations show well-separated clusters representing ear images of the different subjects. This indicates that discriminative and ear-specific features are learned when applying our proposed learning strategies.

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Section: Related Workmentioning

confidence: 99%

Deep Convolutional Neural Networks for Unconstrained Ear Recognition

et al. 2020

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“…In this section, we review traditional ear detection methods along with CNN‐based detection methods. Emeršič et al and Chowdhury et al thoroughly reviewed the techniques in literature for automatic ear detection and recognition. These survey papers also included the benchmark datasets for ear recognition research.…”

Section: Related Workmentioning

confidence: 99%

Unconstrained ear detection using ensemble‐based convolutional neural network model

Ganapathi

Prakash

Dave

et al. 2019

Concurrency and Computation

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This paper presents a technique for ear detection from 2D profile face images that is capable of significantly reducing the false positives. In an ear biometrics system, recognition performance highly depends on the performance of the ear detection module. The trade-off between the complexity and the false positive detection is one of the essential component, where the complexity of a system increases proportionally to achieve a zero false positive rate detection.In literature, available ear detection techniques based on handcrafted features face challenges with low-quality acquired images affected by illumination, occlusion, and pose variations. We propose an ear detection technique using ensemble of convolutional neural network (CNN). The first part of the technique trains three models of CNN on a given dataset, whereas in later part, weighted average of the outputs of trained models is utilized to detect the ear regions. The used ensemble models show better performance as compared to the case when each individual model is used standalone. The proposed technique is being evaluated on two databases, viz, IIT Indore-Collection A (IIT-Col A) database and annotated web ear (AWE) database. Experimental results of ear detection demonstrate the superior performance of the proposed technique over other state-of-the-art techniques in handling illumination, occlusion, and pose variations. KEYWORDS deep learning, ear detection, ensemble model, unconstrained environment INTRODUCTIONIn recent years, ear biometrics has been considered a reliable biometrics and has got attention over face mainly due to its invariant behavior to the age of a person. Biometrics systems based on face have to consider the changes occurring in the face with a change in age or expression.The ear of a person remains consistent in its shape and it is fixed in its position at the center of the profile image. To obtain reliable performance, face images are required to be captured in the controlled setting, while in case of ear, it can be captured with a foreseeable background due to its fixed side face background. An ear biometric system mainly consists of two processes, ie, Ear detection and Ear recognition. Majority of the ear biometrics techniques in the literature use manually cropped ear images to perform ear recognition process. Furthermore, the ear biometric techniques that crops ear automatically are able to detect ear only when the ear has small variations in the side face image. In cases of pose or scale variations, these techniques are found to be inefficient. Additionally, in a real-time environment, these techniques lack in being fully automatic approaches and speed.Performance of an ear detection technique based on handcrafted feature learning methods depends on the dataset used for learning the features. 1 These methods are prone to produce erroneous results when used with the images captured in unconstrained settings like different illumination, background, and pose. Tasks like extraction of suitable features along with the discriminati...

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“…The planes of projection are divided into L × L bins and a distribution matrix is computed by counting the number of points falling in each bin. Furthermore, for the obtained distribution matrix, lower-order central moments μ 11 , μ 12 , μ 21 , μ 22 (6), and entropy e (7) is computed to form a feature vector. Given a distribution matrix D, the central moment μ mn can be defined as follows:…”

Section: D Feature Descriptormentioning

confidence: 99%

“…Although ear has rich and unique characteristics, fast and accurate recognition of the ear is still a challenging task. Although in the literature, there exist several works on constrained and unconstrained ear recognition [6][7][8][9][10], the practical use of 2D ear recognition in real applications is still a hurdle due to its poor recognition accuracy. It is found that poor recognition accuracy due to common problems such as pose variations, illumination, and scaling is easily overcome by threedimensional (3D) images.…”

Section: Introductionmentioning

confidence: 99%

Ear recognition in 3D using 2D curvilinear features

et al. 2018

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This study presents a novel approach for human recognition using co-registered three-dimensional (3D) and 2D ear images. The proposed technique is based on local feature detection and description. The authors detect feature key-points in 2D ear images utilising curvilinear structure and map them to the 3D ear images. Considering a neighbourhood around each mapped key-point in 3D, a feature descriptor vector is computed. To match a probe 3D ear image with a gallery 3D ear image for recognition, first highly similar feature key-points of these images are used as correspondence points for an initial alignment. Afterwards, a fine iterative closest point matching is performed on entire data of the 3D ear images being matched. An extensive experimental analysis is performed to demonstrate the recognition performance of the proposed approach in the presence of noise and occlusions, and compared with the available state-of-the-art 3D ear recognition techniques. The recognition rate of the proposed technique is found to be 98.69% on the University of Notre Dame-Collection J2 dataset with an equal error rate of 1.53%.

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On Applicability of Tunable Filter Bank Based Feature for Ear Biometrics: A Study from Constrained to Unconstrained

Cited by 34 publications

References 94 publications

Deep Convolutional Neural Networks for Unconstrained Ear Recognition

Deep Convolutional Neural Networks for Unconstrained Ear Recognition

Unconstrained ear detection using ensemble‐based convolutional neural network model

Ear recognition in 3D using 2D curvilinear features

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