Hash-based space partitioning approach to iris biometric data indexing | 2019

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In spite of high efficiency of the iris recognition systems, the accuracy of the system degrades with the increase in the size of database, hence there is need for indexing. One of the important characteristics of such an indexing approach is to have high tolerance against feature deviation due to noise. In this work, an indexing approach has been proposed which deals with the feature deviation due to variation in quality of iris images. 

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An advanced fingerprint matching using minutiae-based indirect local features | 2018

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Biometric systems examine the uniqueness of an individual based on physical and behavioral characteristics. Among the known traits, fingerprint is the most significant biometric trait due to its ease of use and high accuracy. However, the efficiency of the fingerprint matching technique depends on the feature vector it uses.

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Watermarking through image geometry change tracking | 2018

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Most of the digital image watermarking techniques are susceptible to geometric attacks such as cropping, rotation and scaling. These attacks are the easiest yet most successful in rendering the survival of watermark difficult. Such geometric operations alter the pixel orientation in the cover thereby rendering the watermark difficult to locate and extract. 

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Locality sensitive hashing based space partitioning approach for indexing multidimensional feature vectors of fingerprint image data | 2018

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In recent years, biometric applications have significantly gained popularity. Such applications involve voluminous databases of high dimensional data. These enormous databases increase the cost of identification and degrade the system performance. To resolve such an issue a plethora of algorithms based on geometric hashing, k–d tree, k-means clustering, etc., have been proposed in the literature.