Методы распознавания личности на основе анализа характеристик наружного уха (Обзор)

To read the full-text of this research, you can request a copy directly from the authors.


The article describes approaches to extracting biometric parameters of the ear in two-dimensional and three-dimensional images, and the basis of measurements of the transfer functions of the ear canal. The methods used for pattern recognition for the construction of means of biometric identification and authentication according to the parameters of the auricle are considered. The main research results in this area are presented.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

ResearchGate has not been able to resolve any citations for this publication.
Full-text available
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.
Full-text available
Convolutional neural networks (CNNs) based deep features have been demonstrated with remarkable performance in various computer vision tasks, such as image classification and face verification. Compared with the hand-crafted descriptors, deep features exhibit more powerful representation ability. Typically, higher layer features contain more semantic information, while lower layer features can provide more low level description. In addition, it turns out that the fusion of different layer features will lead to superior performance. In this paper, we investigate a novel approach for human ear identification by combining hierarchical deep features. First, hierarchical deep features are extracted from ear images using CNN models pre-trained on large scale dataset. To enhance the feature representation and reduce the high dimension of deep features, the Discriminant Correlation Analysis (DCA) algorithm is adopted for fusing deep features from different layers for further improvement. Due to the lack of ear images per person, we propose to transform the ear identification problem to the binary classification by composing pairwise samples and resolve it with the pairwise SVM. Experiments are conducted on four public databases: USTB I, USTB II, IIT Delhi I and IIT Delhi II. The proposed method achieves promising recognition rate and exhibits decent performance in comparison to the state-of- he-art methods. Keywords: Ear recognition, Feature level fusion, CNNs, Pairwise SVM.
Full-text available
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.
Full-text available
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. Owing to these qualities, the interest in ear recognition systems has grown significantly in recent years. In this survey, the authors categorise and summarise approaches to ear detection and recognition in 2D and 3D images. Then, they provide an outlook over possible future research in the field of ear recognition, in the context of smart surveillance and forensic image analysis, which they consider to be the most important application of ear recognition characteristic in the near future.
Full-text available
Unique Biometric Identifiers offer a very convenient way for human identification and authentication. In contrast to passwords they have hence the advantage that they can not be forgotten or lost. In order to set-up a biometric identification/authentication system, reference data have to be stored in a central database. As biometric identifiers are unique for a human being, the derived templates comprise unique, sensitive and therefore private information about a person. This is why many people are reluctant to accept a system based on biometric identification. Consequently, the stored templates have to be handled with care and protected against misuse [1, 2, 3, 4, 5, 6]. It is clear that techniques from cryptography can be used to achieve privacy. However, as biometric data are noisy, and cryptographic functions are by construction very sensitive to small changes in their input, and hence one can not apply those crypto techniques straightforwardly. In this paper we show the feasibility of the techniques developed in [5], [6] by applying them to experimental biometric data. As biometric identifier we have choosen the shape of the inner ear-canal, which is obtained by measuring the headphone-to-ear-canal Transfer Functions (HpTFs) which are known to be person dependent [7].
Conference Paper
Full-text available
Ear detection from a profile face image is an important step in many applications including biometric recognition. But accurate and rapid detection of the ear for real-time applications is a challenging task, particularly in the presence of occlusions. In this work, a cascaded AdaBoost based ear detection approach is proposed. In an experiment with a test set of 203 profile face images, all the ears were accurately detected by the proposed detector with a very low (5 x 10<sup>-6</sup>) false positive rate. It is also very fast and relatively robust to the presence of occlusions and degradation of the ear images (e.g. motion blur). The detection process is fully automatic and does not require any manual intervention.
Smart wearable devices have recently become one of the major technological trends and been widely adopted by the general public. Wireless earphones, in particular, have seen a skyrocketing growth due to its great usability and convenience. With the goal of seeking a more unobtrusive wearable authentication method that the users can easily use and conveniently access, in this study we present EarEcho as a novel, affordable, user-friendly biometric authentication solution. EarEcho takes advantages of the unique physical and geometrical characteristics of human ear canal and assesses the content-free acoustic features of in-ear sound waves for user authentication in a wearable and mobile manner. We implemented the proposed EarEcho on a proof-of-concept prototype and tested it among 20 subjects under diverse application scenarios. We can achieve a recall of 94.19% and precision of 95.16% for one-time authentication, while a recall of 97.55% and precision of 97.57% for continuous authentication. EarEcho has demonstrated its stability over time and robustness to cope with the uncertainties on the varying background noises, body motions, and sound pressure levels.
Object detection and segmentation represents the basis for many tasks in computer and machine vision. In biometric recognition systems the detection of the region-of-interest (ROI) is one of the most crucial steps in the processing pipeline, significantly impacting the performance of the entire recognition system. Existing approaches to ear detection, are commonly susceptible to the presence of severe occlusions, ear accessories or variable illumination conditions and often deteriorate in their performance if applied on ear images captured in unconstrained settings. To address these shortcomings, we present a novel ear detection technique based on convolutional encoder-decoder networks (CEDs). We formulate the problem of ear detection as a two-class segmentation problem and design and train a CED-network architecture to distinguish between image-pixels belonging to the ear and the non-ear class. Unlike competing techniques, our approach does not simply return a bounding box around the detected ear, but provides detailed, pixel-wise information about the location of the ears in the image. Experiments on a dataset gathered from the web (a.k.a. in the wild) show that the proposed technique ensures good detection results in the presence of various covariate factors and significantly outperforms competing methods from the literature.
Conference Paper
State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [7] and Fast R-CNN [5] have reduced the running time of these detection networks, exposing region pro-posal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully-convolutional network that simultaneously predicts object bounds and objectness scores at each position. RPNs are trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. With a simple alternating optimization, RPN and Fast R-CNN can be trained to share convolu-tional features. For the very deep VGG-16 model [18], our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007 (73.2% mAP) and 2012 (70.4% mAP) using 300 proposals per image. The code will be released.
We present a complete three-dimensional (3-D) ear recognition system combining local and holistic features in a computationally efficient manner. The system is comprised of four primary components, namely: 1) ear image segmentation; 2) local feature extraction and matching; 3) holistic feature extraction and matching; and 4) a fusion framework combining local and holistic features at the match score level. For the segmentation component, we introduce a novel shape-based feature set, termed the Histograms of Indexed Shapes (HIS), to localize a rectangular region containing the ear. For the local feature extraction and representation component, we extend the HIS feature descriptor to an object-centered 3-D shape descriptor, the Surface Patch Histogram of Indexed Shapes (SPHIS), for local ear surface representation and matching. For the holistic matching component, we introduce a voxelization scheme for holistic ear representation from which an efficient, voxel-wise comparison of gallery-probe model pairs can be made. The match scores obtained from both the local and holistic matching components are fused to generate the final match scores. Experimental results conducted on the University of Notre Dame (UND) collection G dataset, containing range images of 415 subjects yielded a rank-one recognition rate of 98.3% and an equal error rate of 1.7%. These results demonstrate that the proposed approach outperforms state-of-the-art 3-D ear biometric systems. Additionally, the method is considerably more efficient compared to the state-of-the-art because it employs a sparse set of features rather than using the dense model.
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. KeywordsBiometrics–Ear recognition–Image enhancement–Fusion
Conference Paper
In this paper, we propose the ear detection approach under complex background which has two stages: off-line cascaded classifier training and on-line ear detection. In the stage of off-line training, considering the unique contour, the concave and convex of the ear, we apply the extended haar-like features to construct the space of the weak classifiers using the nearest neighbor norms. And then we choose the gentle AdaBoost algorithm to train the strong classifiers which form the cascaded multi-layer ear detector. In the stage of on-line detection, we apply two methods to speed up the detection procedure. The first one is to adjust the threshold of the strong classifiers to remain the like-ear sub windows for further processing only using the first two layer classifiers. The second one is to keep the size of the original image while scaling the detection sub-windows to locate the ear part. The ear detection experiments on USTB ear database, CAS-PEAL face database and CMU PIE database show that the proposed method is significantly efficient and robust.
Conference Paper
Ears are a new biometric with major advantage in that they appear to maintain their structure with increasing age. Most current approaches are holistic and describe the ear by its general properties. We propose a new model-based approach, capitalizing on explicit structure and with the advantages of being robust in noise and occlusion. Our model is a constellation of generalized ear parts, which is learned off-line using an unsupervised learning algorithm over an enrolled training set of 63 ear images. The Scale Invariant Feature Transform (SIFT), is used to detect the features within the ear images. In recognition, given a profile image of the human head, the ear is enrolled and recognised from the parts selected via the model. We achieve an encouraging recognition rate, on an image database selected from the XM2VTS database. A head-to-head comparison with PCA is also presented to show the advantage derived by the use of the model in successful occlusion handling.
A review of recent advances in 3D ear-and expression-invariant face biometrics // ACM Computing Surveys (CSUR)
  • S Islam
  • M Bennamoun
  • R A Owens
  • R Davies
Islam S., Bennamoun M., Owens R. A., Davies R. A review of recent advances in 3D ear-and expression-invariant face biometrics // ACM Computing Surveys (CSUR). 2012. № 44 (3).
Biometric recognition using 3D ear shape // IEEE Transactions on pattern analysis and machine intelligence
  • P Yan
  • K W Bowyer
Yan P., Bowyer K. W. Biometric recognition using 3D ear shape // IEEE Transactions on pattern analysis and machine intelligence. 2007. № 29 (8). P. 1297-1308.
Forensic identification series: ear identification // Paramont Publishing Company, California
  • A V Iannarelli
Iannarelli A. V. Forensic identification series: ear identification // Paramont Publishing Company, California. 1989. № 5. P. 213.
Perspective methods of human identification: ear biometrics. Opto-electronics review
  • M Choraś
Choraś M. Perspective methods of human identification: ear biometrics. Opto-electronics review. 2008. № 16 (1). P. 85-96. 13. Jeges E., Máté L. Model-based human ear identification: In 2006 World Automation Congress. 2006, July. P. 1-6.
Force field feature extraction for ear biometrics. Computer Vision and Image Understanding
  • D J Hurley
  • M S Nixon
  • J Carter
Hurley D. J., Nixon M. S., Carter J. N. Force field feature extraction for ear biometrics. Computer Vision and Image Understanding. 2005. № 98 (3). P. 491-512.
Ear authentication using Log-Gabor wavelets: Biometric Technology for Human Identification IV
  • A Kumar
  • D Zhang
  • April
Kumar A., Zhang D. April. Ear authentication using Log-Gabor wavelets: Biometric Technology for Human Identification IV. 2007. V. 6539. P. 65390A. International Society for Optics and Photonics.
Human ear recognition in 3D // IEEE Transactions on Pattern Analysis and Machine Intelligence
  • H Chen
  • B Bhanu
Chen H., Bhanu B. Human ear recognition in 3D // IEEE Transactions on Pattern Analysis and Machine Intelligence. 2007. № 29 (4). P. 718-737.
Efficient detection and recognition of 3D ears // International Journal of Computer Vision
  • S M Islam
  • R Davies
  • M Bennamoun
  • A S Mian
Islam S. M., Davies R., Bennamoun M., Mian A. S. Efficient detection and recognition of 3D ears // International Journal of Computer Vision. 2011. № 95 (1). P. 52-73.
National Technology and Engineering Solutions of Sandia LLC, 1998. Systems and methods for biometric identification using the acoustic properties of the ear canal
  • Y Zhang
  • Z Mu
  • L Yuan
  • H Zeng
  • L Chen
Zhang Y., Mu Z., Yuan L., Zeng H., Chen L. 3D ear normalization and recognition based on local surface variation // Applied Sciences. 2017. № 7 (1). P. 104. 25. Bouchard A. M., Osbourn G. C. National Technology and Engineering Solutions of Sandia LLC, 1998. Systems and methods for biometric identification using the acoustic properties of the ear canal. U.S. Patent 5,787,187.