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Semantic ear feature reduction for source camera identification

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Abstract

Energy based Bior 4.4 feature is proven suitable for identifying source camera of ear biometric images when a small number of distinct camera sources are used. This level-2 Bior 4.4 feature vector bears 36 energy values. In this paper, a semantic way of reducing this feature vector is discussed which is capable of identifying the source camera of ear biometric images. We analyze the consequences of the reduction towards performance in terms of accuracy. Based on the mean of variances of wavelet energy feature, the size of the feature vector is gradually reduced. Reduction of accuracy of source camera identification is expected with reduction of the feature vector size. However interestingly, we can remove less important feature dimensions without affecting the accuracy much. We need to ensure preserving the feature indices that are deciding factors in yielding the accuracy. From the experiment on 3-class source camera classification, it has been found that even the feature size can be reduced to 1/3rd (i.e. up to 12 values from 36 values) with a tolerance of only 1% degradation in accuracy. Hence we grossly conclude that very low dimensional feature can be potent to predict source camera blindly with good accuracy.

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Abbreviations

AEF:

: Average of Values at Each Index of the Energy Feature

AMI:

: Mathematical Analysis of Images

FV:

: Feature Vector

IITD:

: Indian Institute of Technology Delhi

PC:

: Principle Component

PCA:

: Principle Component Analysis

PRC:

: Precision Recall Curve

ROC:

: Receiver Operating Characteristic

VEF:

: Variance of Values at Each Index of the Energy Feature

WPUT:

: The West Pomeranian University of Technology

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Acknowledgments

This paper is an extension of our previous work Chowdhury, D.P., Bakshi, S., Sa, P.K., Majhi, B.: ‘Wavelet energy feature based source camera identification for ear biometric images’, Pattern Recognition Letters, 2018, https://doi.org/10.1016/j.patrec.2018.10.009.

Funding

This research is partially supported by the following projects: (1) 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. (2) Information Security Education & Awareness Project (Phase II), Ministry of Electronics and Information Technology (MeitY), Government of India.

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Authors and Affiliations

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

    Debbrota Paul Chowdhury, Sambit Bakshi, Pankaj Kumar Sa & Banshidhar Majhi

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  1. Debbrota Paul Chowdhury
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  2. Sambit Bakshi
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  3. Pankaj Kumar Sa
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  4. Banshidhar Majhi
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Correspondence to Debbrota Paul Chowdhury.

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Chowdhury, D.P., Bakshi, S., Sa, P.K. et al. Semantic ear feature reduction for source camera identification. Multimed Tools Appl 79, 35315–35331 (2020). https://doi.org/10.1007/s11042-019-7665-6

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