Abstract-The success of iris recognition depends mainly on two factors: image acquisition and an iris recognition algorithm. In this study, we present a system that considers both factors and focuses on the latter. The proposed algorithm aims to find out the most efficient wavelet family and its coefficients for encoding the iris template of the experiment samples. The algorithm implemented in software performs segmentation, normalization, feature encoding, data storage, and matching. By using the Haar and Biorthogonal wavelet families at various levels feature encoding is performed by decomposing the normalized iris image. The vertical coefficient is encoded into the iris template and is stored in the database. The performance of the system is evaluated by using the number of degrees of freedom, False Reject Rate (FRR), False Accept Rate (FAR), and Equal Error Rate (EER) and the metrics show that the proposed algorithm can be employed for an iris recognition system.

The purpose of this study is to design a system that will capture the iris image and develop a reliable feature extraction algorithm for iris recognition system. The proposed system is a complete iris recognition system with hardware and software components in which the focus is on the implementation of algorithm based on wavelet transforms. The system consists of the video camera that is interfaced through a frame grabber using the MATLAB program to capture an image of the human eye. The camera includes adjustable chin support, NIR filter and NIR diodes for the lighting and distance settings. The algorithm implemented in software performs segmentation, normalization, feature encoding, and matching. The feature encoding is performed by decomposing the normalized 24 x 240 pixels iris image using Haar and Biorthogonal wavelet families at various levels. The vertical coefficients are encoded into iris templates and stored at the database. The system is evaluated in two modes: verification and identification. The HD values are used as threshold levels to identify the iris image. The number of degrees of freedom is calculated for inter-class comparisons. The test results at different coefficients show that in terms of efficiency, the Haar wavelet decomposition at level 4 is the highest with a Correct Recognition Rate (CRR) of 98% at a feature vector length of 120 bits. The Equal Error Rate (ERR) of the system is 2%. The metrics show that the proposed system provides highly accurate recognition rates and suggest the most appropriate choices that need to be made for best results.

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An electromyography signal that was taken from the surface of the stump-muscle of an amputee is used to control over the myoelectric prosthesis. We present a method of acquiring these signals over the surface of the skin by using a surface-EMG electrode connected to it. In this study, a five fingered prosthetic hand, actuated by five motors, one motor for each finger was used to simulate some of the hand action movements and assess its capability of giving control over each hand action movement. The study was concerned with the signal acquisition that controls the myoelectric prosthesis. ICA was applied to separate mutually independent components that are the result of surface electromyography signals which provided a promising method in the classification of hand action movement based on each level of muscle contraction. The study determined the pattern of each hand action based on the correlation between estimated percent Muscle Voluntary Contraction (%MVC) vs. degree of movement of the motor, and the correlation between the motor frequencies vs. the degree of movement of the motor.