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Iris recognition is receiving increasing attention as a means of personal recognition. Statistical methods, namely Single Value Decomposition (SVD), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are employed to extract the iris feature from a pattern named IrisPattern based on the iris image. These extracted patterns a...
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... more difficult to change the iris structure without risking vision damage, and therefore, avoids potential intruders from modifying iris characteristics to defraud the recognition system [3]. These advantages let iris recognition be one of the most accurate and reliable biometric for identification. A front-on view of the human eye is shown in Fig. 1. However, iris recognition has also some disadvantages. Generally, some parts of the iris are usually occluded by the eyelid and eyelash. There are four main stages of the iris recognition system namely image acquisition, image segmentation, feature extraction and Classification. Each of these stages uses different algorithms. Image ...
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In this research, a neural network using backpropagation (BPNN) algorithm was trained and learned to work as the cone cells in human eyes to recognize the three fundamental cells' colors and hues, as the neural network showed good results in training and testing the color feature it was trained and learned again to recognize two nature scenes image...
An eye image has useless parts such as eyelid and eyelash apart from iris. These useless parts are masked and iris region alone is detected by Geodesic Active Contour Algorithm (GAC). Independent Component Analysis (ICA) is used to extract texture features in the iris. The best features (small subset) are then selected from the extracted texture fe...
Citations
... The picture of the iris is first transformed into a binary form by adding an acceptable threshold value. This is followed by an edge detector algorithm such as a canny edge [19]. Eventually, the Hough Transform is applied to the edge picture to discover a pupil circle. ...
Biometric recognition refers to an automated identification of individuals based on a feature vector(s) extracted from their physiological or behavioral trait. A multimodal biometric authentication system can be taken as a traditional information fusion so that we can improve the overall decision accuracy for the system. Those biometric authentication system that use more than one physiological trait for enrolment or identification in applications such as entry/exit on the border, ATM or access control, multi-modal biometric systems are looked reducing false acceptance and false rejection rates, presenting an ancillary means of enrolment, and identification if adequate data cannot be obtained from a given biometric specimen and disputing attacks to fool the biometric systems through counterfeit data sources such as synthetic iris images. In this research paper, proposed model provides high security in authentication which protects service user from unauthorized access. In this proposed model, user is required to authenticate himself with biometric identification (Iris recognition) and Personal Identification Number (PIN). This model reduces complexity with authentication as “authentication is always with you” with high security. It also saves time and efforts compared with card based ATMs and also saves environmental pollution problem of excess number of plastic cards.
