Large-scale identity projects such as the Unique Identification Authority of India (UIDAI) comprise of multiple individual organizations, which may use different sensors for enrolling the individuals while the data obtained at the time of verification can be collected from a different sensor. In such multi-camera scenario, it is imperative to perform image-based iris sensor identification. In this research, we propose an efficient algorithm to identify the sensor from which the iris image is captured. The proposed algorithm is the amalgamation of SVM fitness function based Bacteria Foraging (BF) feature selection and fusion of multiple features such as Block Image Statistical Measure (BISM), High Order Wavelet Entropy (HOWE), Texture Measure (TM), Single-level Multi-orientation Wavelet Texture (SlMoWT), and Image Quality Measures (IQM). The selected features are then given input to a supervised classification algorithm for iris sensor identification. The second contribution of this research is developing two sets of multisensor iris image databases that, in total, contain 6000 images with over 150 subjects. The results show that the proposed sensor classification algorithm is computationally very fast and yields an accuracy of over 99% on multiple databases.
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.