Wavelet energy feature based source camera identification for ear biometric images

Abstract In this paper a source camera identification algorithm for ear biometric images has been proposed based on tunable filter bank as a feature extractor. Maintaining the frequency selectivity property, distinct features are extracted by this filter bank, based on a half-band polynomial of 14th order. With the help of four ear databases, it is demonstrated that tunable filter bank based features correctly identify the sources of ear images with an average accuracy of 99.25% when there are limited number of camera sources available. It is also shown that accuracy would fall when significantly large number of cameras are introduced to acquire ear images. Depending on the experimental results, it can be well concluded that tunable filter bank based feature, apart from its recognition performance, is also a promising candidate to support forensic validation of camera source.

[1]  Peter Peer,et al.  Ear recognition: More than a survey , 2016, Neurocomputing.

[2]  Nasir D. Memon,et al.  Blind source camera identification , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[3]  Alex ChiChung Kot,et al.  Accurate Detection of Demosaicing Regularity for Digital Image Forensics , 2009, IEEE Transactions on Information Forensics and Security.

[4]  Nicu Sebe,et al.  The Many Shades of Negativity , 2017, IEEE Transactions on Multimedia.

[5]  Sambit Bakshi,et al.  Security through human-factors and biometrics , 2013, SIN.

[6]  Yun Q. Shi,et al.  Camera Model Identification Using Local Binary Patterns , 2012, 2012 IEEE International Conference on Multimedia and Expo.

[7]  Mohan S. Kankanhalli,et al.  Identifying Source Cell Phone using Chromatic Aberration , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[8]  Min Wu,et al.  Nonintrusive component forensics of visual sensors using output images , 2007, IEEE Transactions on Information Forensics and Security.

[9]  Lina Yao,et al.  Diagnosis Code Assignment Using Sparsity-Based Disease Correlation Embedding , 2016, IEEE Transactions on Knowledge and Data Engineering.

[10]  Yi Yang,et al.  Semantic Pooling for Complex Event Analysis in Untrimmed Videos , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Guodong Guo,et al.  On Applicability of Tunable Filter Bank Based Feature for Ear Biometrics: A Study from Constrained to Unconstrained , 2017, Journal of Medical Systems.

[12]  Jessica J. Fridrich,et al.  Rich Models for Steganalysis of Digital Images , 2012, IEEE Transactions on Information Forensics and Security.

[13]  Matthew C. Stamm,et al.  Camera model identification framework using an ensemble of demosaicing features , 2015, 2015 IEEE International Workshop on Information Forensics and Security (WIFS).

[14]  Hany Farid,et al.  Digital Image Authentication From JPEG Headers , 2011, IEEE Transactions on Information Forensics and Security.

[15]  Marc Chaumont,et al.  Source Camera Model Identification Using Features from Contaminated Sensor Noise , 2015, IWDW.

[16]  Luisa Verdoliva,et al.  A study of co-occurrence based local features for camera model identification , 2016, Multimedia Tools and Applications.

[17]  Chenye Wu,et al.  Automated human identification using ear imaging , 2012, Pattern Recognit..

[18]  Lina Yao,et al.  Learning Multiple Diagnosis Codes for ICU Patients with Local Disease Correlation Mining , 2017, ACM Trans. Knowl. Discov. Data.

[19]  Nasir D. Memon,et al.  Source camera identification based on CFA interpolation , 2005, IEEE International Conference on Image Processing 2005.

[20]  Dariusz Frejlichowski,et al.  The West Pomeranian University of Technology Ear Database - A Tool for Testing Biometric Algorithms , 2010, ICIAR.

[21]  Che-Yen Wen,et al.  Image authentication for digital image evidence , 2006 .

[22]  Edmund Y. Lam,et al.  Source camera identification using footprints from lens aberration , 2006, Electronic Imaging.

[23]  Ats Ho,et al.  Inter-Camera Model Image Source Identification with Conditional Probability Features , 2012 .

[24]  Miroslav Goljan,et al.  Digital camera identification from sensor pattern noise , 2006, IEEE Transactions on Information Forensics and Security.

[25]  Nicu Sebe,et al.  Joint Attributes and Event Analysis for Multimedia Event Detection , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[26]  Miroslav Goljan,et al.  Using sensor pattern noise for camera model identification , 2008, 2008 15th IEEE International Conference on Image Processing.

[27]  G. O. Williams,et al.  The use of d' as a “decidability” index , 1996, 1996 30th Annual International Carnahan Conference on Security Technology.

[28]  Marc Chaumont,et al.  Camera model identification based machine learning approach with high order statistics features , 2016, 2016 24th European Signal Processing Conference (EUSIPCO).

[29]  Husrev T. Sencar,et al.  Source Camera Identification Based on Sensor Dust Characteristics , 2007 .