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

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

In this paper, an overall framework has been presented for person verification using ear biometric which uses tunable filter bank as local feature extractor. The tunable filter bank, based on a half-band polynomial of 14th order, extracts distinct features from ear images maintaining its frequency selectivity property. To advocate the applicability of tunable filter bank on ear biometrics, recognition test has been performed on available constrained databases like AMI, WPUT, IITD and unconstrained database like UERC. Experiments have been conducted applying tunable filter based feature extractor on subparts of the ear. Empirical experiments have been conducted with four and six subdivisions of the ear image. Analyzing the experimental results, it has been found that tunable filter moderately succeeds to distinguish ear features at par with the state-of-the-art features used for ear recognition. Accuracies of 70.58%, 67.01%, 81.98%, and 57.75% have been achieved on AMI, WPUT, IITD, and UERC databases through considering Canberra Distance as underlying measure of separation. The performances indicate that tunable filter is a candidate for recognizing human from ear images.

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Abbreviations

AWE:

Annotated Web Ears

BSIF:

Binarized Statistical Image Features

CD:

Canbera Distance

CS:

Cosine Similarity

CDF:

Cohen-Daubechies-Feauveau

CER:

Crossover Error Rate

CNN:

Convolutional Neural Network

DET:

Detection Error Tradeoff

DSIFT:

Dense Scale-Invariant Feature Transform

DWT:

Discrete Wavelet Transform

EER:

Equal Error Rate

FAR:

False Acceptance Rate

FRR:

False Rejection Rate

FV:

Feature Vector

GAR:

Genuine Acceptance Rate

HOG:

Histograms of Oriented Gradients

ICA:

Independent Component Analysis

ICP:

Iterative Closest Point

LBP:

Local Binary Patterns

LDA:

Linear Discriminant Analysis

LPQ:

Local Phase Quantization

NN:

Nearest Neighbor

PCA:

Principal Component Analysis

PIFS:

Partitioned Iterated Function System

POEM:

Patterns of Oriented Edge Magnitudes

RBF:

Radial Basis Function

RILPQ:

Rotation Invariant Local Phase Quantization

ROC:

Receiver Operating Characteristic

SDP:

Semidefinite Programming

SIFT:

Scale Invariant Feature Transform

SURF:

Speeded Up Robust Features

UERC:

Unconstrained Ear Recognition Challenge.

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Acknowledgements

This research is partially supported by the following project: Grant No. ETI/359/2014 by Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions (FIST) Program 2016, Department of Science and Technology, Government of India.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, National Institute of Technology Rourkela, Odisha, 769 008, India

    Debbrota Paul Chowdhury, Sambit Bakshi & Pankaj Kumar Sa

  2. Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, 26506, USA

    Guodong Guo

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  1. Debbrota Paul Chowdhury
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  2. Sambit Bakshi
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  3. Guodong Guo
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  4. Pankaj Kumar Sa
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Correspondence to Sambit Bakshi.

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This article is part of the Topical Collection on Convergence of Deep Machine Learning and Nature Inspired Computing Paradigms for Medical Informatics

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Chowdhury, D.P., Bakshi, S., Guo, G. et al. On Applicability of Tunable Filter Bank Based Feature for Ear Biometrics: A Study from Constrained to Unconstrained. J Med Syst 42, 11 (2018). https://doi.org/10.1007/s10916-017-0855-8

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