Most commercially available iris recognition systems are based on the pioneered algorithms of Daugman [13] and Wildes [50]. However, they perform well for ideal conditions but may fail for non-ideal data. The non-ideal eye images may contain multiple issues such as specular reflections, low contrast, blurring, focus, non-uniform illumination, glasses and contact lens, off-axis and the off-angle eyes; occlusions such as eyelashes, eyelids and hair [64]. Jan, F., et al. [71], proposed robust iris localization algorithm for non-ideal eye image. Their algorithm involves two phase. In the first phase, they used Hough transform, gray level statistics, adaptive thresholding, and a geometrical transform to extract the pupil circle in a sub-image containing a coarse pupil region. After that, they localize iris circle in a sub-image centered at the pupil circle. However, if the first phase fails, the second phase starts, where first they localize a coarse iris region in the eye image. Next, extract pupil circle within the coarse iris region by reusing procedure of first phase. Following that, iris circle is localized. Finally, the iris circular boundaries are regularized by using radial gradients and the active contours. Experimental results show that their proposed technique is tolerant to off-axis eye images, specular reflections, non-uniform illumination; glasses, contact lens, hair, eyelashes, and eyelids occlusions.
Li, P., et. al [72] also introduce a robust iris segmentation algorithm based on the Random Sample Consensus (RANSAC). According to their experiment, this algorithm can localize the iris boundaries more accurately than the methods based on the Hough transform. Li, P., et. al[73], presented a weighted co-occurrence phas...
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...- square distance, Euclidean distance for feature classification. The accurate iris centre and radius is determined by the circular edge detector (CED) defined by argmax_((x0,y0),r) [∂/∂r (∫_(π/4)^(π/6)▒〖(I(x,y))/(5πr/12) ds〗+∫_(5π/6)^(5π/4)▒〖(I(x,y))/(5πr/12) ds〗) ]
Where r is the radius of the iris region and coordinates 〖(x〗_0,y_0) denotes the centre position of the iris. Szewczyk et al. [106] also proposed an iris recognition algorithm for noisy iris images based on bi-orthogonal wavelet transform. Zafar et. al. [107] proposed a simple method of segmentation using Gaussian filter followed by canny edge detector after the necessary preprocessing. Feature extraction is then applied on the segmented images, to represent the iris in fewer dimensions. They proposed Curvelets transform for this phase. Support Vector Machine has been used for classification purpose.
Now-a-day, biometric authentication system or simply biometric system, offers a reliable and user-friendly solution to the problem of identity management by establishing the identity of an individual based on “who the person is”, rather than the knowledge-based i.e. “what the person knows" or token-based i.e. “what the person carries” (Jain et al., 1999). The word biometrics is derived from the Greek words bios (meaning life) and metron (meaning measurement); biometric identifiers are measurements
Signature verification, signature recognition, signatures database, and HMM. I. INTRODUCTION Biometrics is the act of science to verify and identify a human being. Biometrics confirmation crown or judge numerous improvements over conventional approaches. Biometrics can be categorized into two types: Behavioral and physiological. Behavioral biometrics including signature verification, keystrokes dynamics. Physiological biometrics including fingerprints and iris characteristics .The signature verification