SIFT-based ear recognition by fusion of detected keypoints from color similarity slice regions

Kisku, Dakshina Ranjan; Mehrotra, Hunny; Gupta, Phalguni; Sing, Jamuna Kanta

doi:10.1109/actea.2009.5227958

Cited by 36 publications

(15 citation statements)

References 22 publications

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“…Using the Carreira-Perpinan database (segmented ear images) [Carreira-Perpinan 1995], they reported a recognition rate of 78.8%. Kisku et al [2009a] used SIFT feature descriptors for structural representation of ear images (as shown in Figure 15). They developed an ear skin color model using Gaussian Mixture Model (GMM) and clustered the ear color pattern using vector quantization.…”

Section: Wavelet Transformationmentioning

confidence: 99%

A survey on ear biometrics

et al. 2013

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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 noncontact 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.

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Section: Wavelet Transformationmentioning

confidence: 99%

A survey on ear biometrics

et al. 2013

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“…2D approaches are more appropriate for our domain because of the field requirements of fast and cheap solutions. Extracting a feature vector from a 2D ear representation has been done in many ways including Eigen Ears (PCA) [Chang et al, 2003], Force Field [Abdel- Mottaleb and Zhou, 2005], GFD [Abate et al, 2006], SIFT/SURF [Cummings et al, 2010, Kisku et al, 2009, and LBPs [Wang et al, 2008, Boodoo-Jahangeer andBaichoo, 2013].…”

Section: Related Workmentioning

confidence: 99%

“…SIFT features [Lowe, 1999] have been successfully used in ear biometrics by [Cummings et al, 2010, Kisku et al, 2009. SIFT features are scale and rotation invariant, and only show some illumination tolerance.…”

Section: Scale Invariant Feature Transformmentioning

confidence: 99%

Image-based Ear Biometric Smartphone App for Patient Identification in Field Settings

Bargal

Welles

Chan

et al. 2015

Proceedings of the 10th International Conference on Computer Vision Theory and Applications

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We present a work in progress of a computer vision application that would directly impact the delivery of healthcare in underdeveloped countries. We describe the development of an image-based smartphone application prototype for ear biometrics. The application targets the public health problem of managing medical records at on-site medical clinics in less developed countries where many individuals do not hold IDs. The domain presents challenges for an ear biometric system, including varying scale, rotation, and illumination. It was not clear which feature descriptors would work best for the application, so a comparative study of three ear biometric extraction techniques was performed, one of which was used to develop an iOS application prototype to establish the identity of an individual using a smartphone camera image. A pilot study was then conducted on the developed application to test feasibility in naturalistic settings.

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“…They used two step algorithm with model template structure and online recognition. Reference [3] developed an ear recognition system by fusing SIFT features of color segmented slice regions of an ear. Reference [4] reported kernel independent component analysis (KICA) and support vector machine (SVM) with Gaussian radial basis function (GRBF) for ear feature extraction and classification.…”

Section: Introductionmentioning

confidence: 99%

A Novel Ear Identification System for Security Applications

Tayyaba¹,

Ashraf²,

Afzulpurkar³

et al. 2013

IJCCE

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Abstract-In this paper, a novel ear identification system for security applications is presented. The structure of human ear is an inimitable characteristic that remains comparatively unchanged with time. The consistent and stout features of ear can be extracted from a distance. The proposed system consists of three modules that are database module, image processing module and identification module. The system takes ear images and extracts useful information by image processing module. The resultant information and templates are stored in data base. The suspect's image after processing by image processing module is sent to identification module for assessment with the record present in database using template matching. Then the identification module makes decision regarding similarity or difference between the processed images. Finally, the decision is sent to report generator component that generates the final report. The system has been tested for 24 ear images by using templates. The results show that the system is 98% accurate.

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SIFT-based ear recognition by fusion of detected keypoints from color similarity slice regions

Cited by 36 publications

References 22 publications

A survey on ear biometrics

A survey on ear biometrics

Image-based Ear Biometric Smartphone App for Patient Identification in Field Settings

A Novel Ear Identification System for Security Applications

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