Toward Noncooperative Iris Recognition: A Classification Approach Using Multiple Signatures

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 29; no. 4; pp. 607 - 612
• Main Authors: Proenca, H., Alexandre, L.A.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2007; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustic reflection; Active control; Algorithms; Artificial Intelligence; biometrics; Biometry - methods; Brightness; Classification; Cluster Analysis; Error analysis; Feature extraction; Focusing; Humans; Image contrast; Image Interpretation, Computer-Assisted - methods; Image recognition; Image segmentation; Intelligence; Iris - anatomy & histology; Iris classification; Iris recognition; Lighting; Lighting control; noncooperative iris recognition; Optical reflection; Pattern Recognition, Automated - methods; Recognition; Reproducibility of Results; Sensitivity and Specificity; Signatures; Subtraction Technique
• ISSN: 0162-8828; 1939-3539
• DOI: 10.1109/TPAMI.2007.1016

This paper focuses on noncooperative iris recognition, i.e., the capture of iris images at large distances, under less controlled lighting conditions, and without active participation of the subjects. This increases the probability of capturing very heterogeneous images (regarding focus, contrast, or brightness) and with several noise factors (iris obstructions and reflections). Current iris recognition systems are unable to deal with noisy data and substantially increase their error rates, especially the false rejections, in these conditions. We propose an iris classification method that divides the segmented and normalized iris image into six regions, makes an independent feature extraction and comparison for each region, and combines each of the dissimilarity values through a classification rule. Experiments show a substantial decrease, higher than 40 percent, of the false rejection rates in the recognition of noisy iris images