Human recognition using 3D ear images

This paper proposes an ear recognition technique which makes use of 3D along with co-registered 2D ear images. It presents a two-step matching technique to compare two 3D ears. In the first step, it computes salient 3D data points from 3D ear images with the help of local 2D feature points of co-reg...

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Published inNeurocomputing (Amsterdam) Vol. 140; pp. 317 - 325
Main Authors Prakash, Surya, Gupta, Phalguni
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 22.09.2014
Elsevier
Subjects
2D and 3D Ear Recognition
Alignment
Applied sciences
Artificial intelligence
Biometrics
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Ear
Exact sciences and technology
Generalized Procrustes Analysis
Iterative Closest Point Technique
Local Feature Points
Matching
Object recognition
Pattern recognition. Digital image processing. Computational geometry
Software
Three dimensional
Three dimensional models
Two dimensional
Biometrics
Local Feature Points
Generalized Procrustes Analysis
2D and 3D Ear Recognition
Iterative Closest Point Technique
Image registration
Computer vision
Image recognition
Error rate
Posture
Image matching
Ear
Tridimensional image
Procrustes analysis
Online AccessGet full text
ISSN0925-2312
1872-8286
DOI10.1016/j.neucom.2014.03.007

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Abstract This paper proposes an ear recognition technique which makes use of 3D along with co-registered 2D ear images. It presents a two-step matching technique to compare two 3D ears. In the first step, it computes salient 3D data points from 3D ear images with the help of local 2D feature points of co-registered 2D ear images. Subsequently, it uses these salient 3D points to coarsely align 3D ear images. In the second step, it performs final matching of coarsely aligned 3D ear images by using a Generalized Procrustes Analysis (GPA) and Iterative Closest Point (ICP) based matching technique (GPA-ICP). The proposed technique has been tested on 1780 images of 404 subjects (two or more images per subject) of University of Notre Dame public database-Collection J2 (UND-J2) which consists of co-registered 2D and 3D ear images with scale and pose variations. It has achieved a verification accuracy of 98.30% with an equal error rate of 1.8%. •Human recognition using 3D along with co-registered 2D ear images.•Use of local 2D feature points for initial 3D data alignment.•Use of Generalized Procrustes Analysis and ICP based technique for robust 3D data matching.•The technique can be used in many applications such as face and object recognition.
AbstractList This paper proposes an ear recognition technique which makes use of 3D along with co-registered 2D ear images. It presents a two-step matching technique to compare two 3D ears. In the first step, it computes salient 3D data points from 3D ear images with the help of local 2D feature points of co-registered 2D ear images. Subsequently, it uses these salient 3D points to coarsely align 3D ear images. In the second step, it performs final matching of coarsely aligned 3D ear images by using a Generalized Procrustes Analysis (GPA) and Iterative Closest Point (ICP) based matching technique (GPA-ICP). The proposed technique has been tested on 1780 images of 404 subjects (two or more images per subject) of University of Notre Dame public database-Collection J2 (UND-J2) which consists of co-registered 2D and 3D ear images with scale and pose variations. It has achieved a verification accuracy of 98.30% with an equal error rate of 1.8%.
This paper proposes an ear recognition technique which makes use of 3D along with co-registered 2D ear images. It presents a two-step matching technique to compare two 3D ears. In the first step, it computes salient 3D data points from 3D ear images with the help of local 2D feature points of co-registered 2D ear images. Subsequently, it uses these salient 3D points to coarsely align 3D ear images. In the second step, it performs final matching of coarsely aligned 3D ear images by using a Generalized Procrustes Analysis (GPA) and Iterative Closest Point (ICP) based matching technique (GPA-ICP). The proposed technique has been tested on 1780 images of 404 subjects (two or more images per subject) of University of Notre Dame public database-Collection J2 (UND-J2) which consists of co-registered 2D and 3D ear images with scale and pose variations. It has achieved a verification accuracy of 98.30% with an equal error rate of 1.8%. •Human recognition using 3D along with co-registered 2D ear images.•Use of local 2D feature points for initial 3D data alignment.•Use of Generalized Procrustes Analysis and ICP based technique for robust 3D data matching.•The technique can be used in many applications such as face and object recognition.
Author Gupta, Phalguni
Prakash, Surya
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Keywords Biometrics
Local Feature Points
Generalized Procrustes Analysis
2D and 3D Ear Recognition
Iterative Closest Point Technique
Image registration
Computer vision
Image recognition
Error rate
Posture
Image matching
Ear
Tridimensional image
Procrustes analysis
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Snippet This paper proposes an ear recognition technique which makes use of 3D along with co-registered 2D ear images. It presents a two-step matching technique to...
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SubjectTerms 2D and 3D Ear Recognition
Alignment
Applied sciences
Artificial intelligence
Biometrics
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Ear
Exact sciences and technology
Generalized Procrustes Analysis
Iterative Closest Point Technique
Local Feature Points
Matching
Object recognition
Pattern recognition. Digital image processing. Computational geometry
Software
Three dimensional
Three dimensional models
Two dimensional
Title Human recognition using 3D ear images
URI https://dx.doi.org/10.1016/j.neucom.2014.03.007
https://www.proquest.com/docview/1793285373
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