Segmentation in Non-ideal Iris Biometric Machines
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Today, the biometric technology plays a vital role in the secure identification of humans, which is a big question at the place of identification. This technology is functioning successfully in different parts around the globe, e.g., in U.A.E., UK, and US. The commonly used biometrics include Face, Fingerprint, Voice, and Palm; however, these change with the environmental or aging effects. For example, the scars, beard, and skin-wrinkles affect the face recognition system performance. On the other hand, the iris is relatively more robust, unique, and non-invasive. Iris biometric systems are fu...
Today, the biometric technology plays a vital role in the secure identification of humans, which is a big question at the place of identification. This technology is functioning successfully in different parts around the globe, e.g., in U.A.E., UK, and US. The commonly used biometrics include Face, Fingerprint, Voice, and Palm; however, these change with the environmental or aging effects. For example, the scars, beard, and skin-wrinkles affect the face recognition system performance. On the other hand, the iris is relatively more robust, unique, and non-invasive. Iris biometric systems are functional in the different parts of globe, however they perform under very strict environment. For example, a subject, wearing no cosmetics, glares directly into the camera view for a while during his/her image acquisition. Notably, such systems perform poorly for the unconstrained environment, e.g., a subject on the move. In non-ideal systems, the acquired eye images contain blurring, reflections, and among others. Due to this reason, a main issue in such systems is of the accurate iris segmentation. This book covers numerous iris segmentation schemes for the non-ideal iris biometric systems
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