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@article{146582,
        author = {NIGILA S and Dr.G.Gandhimathi},
        title = {Robust security using code level approach for iris recognition},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {12},
        pages = {942-948},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=146582},
        abstract = {Matching heterogeneous iris images in less con-strained applications of iris biometrics is becoming a challenging task. The existing solutions try to reduce the difference between heterogeneous iris images in pixel intensities or filtered features. In contrast, this paper proposes a code-level approach in het-erogenous iris recognition. The non-linear relationship between binary feature codes of heterogeneous iris images is modeled by an adapted Markov network. This model transforms the number of iris templates in the probe into a homogenous iris template corresponding to the gallery sample. In addition, a weight map on the reliability of binary codes in the iris template can be derived from the model. The learnt iris template and weight map are jointly used in building a robust iris matcher against the variations of imaging sensors, capturing distance and subject conditions. Extensive experimental results of matching cross-sensor, high-resolution vs low-resolution and, clear vs blurred iris images demonstrate the code-level approach can achieve the highest accuracy in compared to the existing pixel-level, feature-level and score-level solutions},
        keywords = {Homogenous iris, score-level solutions},
        month = {},
        }