Matching of Iris Pattern using Image Processing

Personal identification is very vital in this digital era for simpler mobile phone unlocking to criminal identification in the scene of crime. There are various methods of personal identification ranging from non-invasive methods of presence of moles in the visible parts of the body to the invasive DNA karyotyping. Other in the spectrum being fingerprinting, lip print, foot print, tongue print, palate print etc. As age advances there might be slight variations in finger print, ear biometric etc, where as in iris the amount of pigmentation might vary but the pattern remains almost same from birth to death, unless otherwise there is any injury to the iris which is very remote. Iris pattern recognition is a non-invasive method of biometric identification. Iris architecture is not only complex but also unique to an individual. In this article a methodology is been proposed to match iris pattern.

Use of iris pattern recognition to evaluate ocular torsional changes associated with head tilt

Purpose: To describe the use of enhanced iris images and a computer software program to quantify ocular torsional changes associated with head tilt. Methods: Pixel coordinates of the pupil and different iris landmarks were obtained manually using paint program from digital images of the right and left iris of 3 subjects with normal extraocular motility. Photographs of the right eye and of the left eye were taken in the straight-ahead position and at various degrees of right and left head tilt. A computer software program converted the x- and y-pixel coordinates into angles of rotation after averaging multiple points and determining the degree and the direction of torsion for each eye. The degree of head tilt was mathematically calculated from the digital images. The degree and the direction of ocular torsion were correlated with the degree and the direction of head tilt. Results: The average degree of head tilt was 27.5 degrees (from 8 to 43 degrees). The average intorsion of the lower eye per degree of head tilt was 0.61 degrees (from 0.54 to 0.65 degrees). The average extorsion of the higher eye per degree of head tilt was 0.56 degrees (from 0.43 to 0.60 degrees). The average ocular torsional changes strongly correlated with the degree of head tilt (correlation coefficient = 0.92). Conclusions: Computer-assisted iris pattern recognition and analysis of the ocular torsional changes associated with head tilt may provide a useful and objective means of assessing ocular torsion.