Component-based Thermal Face Recognition
Current Journal of Applied Science and Technology · pp. 945–966 · Published 27 Dec 2013
10.9734/BJAST/2014/6295Abstract
Face recognition based only on the visible spectrum has shown difficulties in performing consistently in uncontrolled operating conditions. Face recognition using different imaging modalities, particularly infrared imaging sensors has become an area of growing interest in recent years. In this paper, we present a new technique for face recognition that exploits the local statistical characteristics of a thermal image. The “whole” face image is divided into components of different sizes. The statistical features of these components, beside the “whole” image are combined together using fusion methods. Decision level fusion finds a combination of multiple statistical patterns to produce an integrated result that is enhanced in terms of information content for pattern recognition and classification. Local representations offer robustness against variability due to the changes in localized regions of the objects. The proposed feature vector consists of different moments’ calculations and thermal components’ histograms. The features found from local analysis are less sensitive to illumination changes, easier for estimating the rotations, have less computational burden and have the potential to achieve higher correct recognition rates. The experimental results reveal that the new system can achieve a success rate of 96.4% when implemented on the AIAOU Database.
References (27)
- 1 Face recognition [DOI]
- 2 A Two-Phase Test Sample Sparse Representation Method for Use With Face Recognition [DOI]
- 3 Multiscale Fusion of Visible and Thermal IR Images for Illumination-Invariant Face Recognition [DOI]
- 4 A comparative analysis of face recognition performance with visible and thermal infrared imagery [DOI]
- 5 Beyond sparsity: The role of L1-optimizer in pattern classification [DOI]
- 6 IR and visible light face recognition [DOI]
- 7 Using the idea of the sparse representation to perform coarse-to-fine face recognition [DOI]
- 8 A Unified Framework for Biometric Expert Fusion Incorporating Quality Measures [DOI]
- 9 Long Range Cross-Spectral Face Recognition: Matching SWIR Against Visible Light Images [DOI]
- 10 Regularized Discriminative Spectral Regression Method for Heterogeneous Face Matching [DOI]
- 11 History, current status, and future of infrared identification [DOI]
- 12 Thermal face recognition in an operational scenario [DOI]
- 13 Thermal Infrared Face Recognition – A Biometric Identification Technique for Robust Security system [DOI]
- 14 Face identification using thermal image processing [DOI]
- 15 Thermal Imaging as a Biometrics Approach to Facial Signature Authentication [DOI]
- 16 Automated thermal face recognition based on minutiae extraction [DOI]
- 17 Thermal Face Recognition Using Local Interest Points and Descriptors for HRI Applications [DOI]
- 18 Facial biometrics for situational awareness systems [DOI]
- 19 Face Recognition in the Thermal Infrared [DOI]
- 20 Probabilistic Bayesian Framework For Infrared Face Recognition [DOI]
- 21 Multispectral Infrared Face Recognition: a comparative study [DOI]
- 22 Normalization of Infrared Facial Images under Variant Ambient Temperatures [DOI]
- 23 Thermal Human Face Recognition Based on Haar Wavelet Transform and Series Matching Technique [DOI]
- 24 Improved Infrared Face Identification Performance Using Nonuniformity Correction Techniques [DOI]
- 25 New Image Fusion-Based Algorithm to Face Recognition [DOI]
- 26 Discriminant Phase Component for Face Recognition [DOI]
- 27 Face Recognition in the Dark [DOI]
Cited by 9
N. Zaeri · Pattern Recognition and Image Analysis · 2020
N. Zaeri · International Conference on Modelling and Simulation · 2016
N. Zaeri · 2014 2nd International Conference on Artificial Intelligence, Modelling and Simulation · 2014
Li Hu · 2018
T. Jayanthi · 2014
N. Zaeri, F. Baker, R. Dib · 2014
Naser Zaeri · 2018 UKSim-AMSS 20th International Conference on Computer Modelling and Simulation (UKSim) · 2018
Showing 7 of 9 known citations — external sources report more than can currently be individually listed.
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