In order to select the effective iris information, iris image quality evaluation is of great importance. pointed out that the notable trait of partial iris recognition algorithms is the inner regions of iris which produce much less identification accuracy than the center or outer regions do however, this does not indicate that inner regions of iris do not contribute to the accuracy of the entire template it simply means that there is less stable, discriminatory information that existed in the inner iris regions.
CASIA IRIS IMAGE DATABASE FREE DOWNLOAD RAR
(2) At the same time, the way of selecting different regions of the iris texture information will influence the RAR because different iris regions contain different texture information. (1) Whether partial iris image can effectively replace the entire iris image or not is still an open question. Unfortunately, this will bring about two issues. Therefore, partial iris recognition algorithms are going to play a significant role.
Currently, most iris recognition systems require a cooperative subject however, capturing the entire iris may be infeasible in surveillance application. Because of its uniqueness and stability, iris recognition is one of the most reliable human identification techniques. From birth to death, the pattern of the iris is relatively constant over a person's lifetime. Today, biometric recognition has become a common and reliable way to authenticate the identity of a living person based on physiological or behavioral characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples' own characteristics. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. Finally, all tracks' information is fused according to the weights of different tracks. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). There are three novelties compared to previous work. We design a control circuit to adjust spectrums automatically.For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. The device supplies an evenly distributed illumination and captures palmprint images using a CCD camera fixed on the bottom of the device. Subjects are required to put his palm into the device and lay it before a uniform-colored background. In our device, there are no pegs to restrict postures and positions of palms. Through that, we aim to increase diversity of intra-class samples and simulate practical use. Between two samples, we allow a certain degree of variations of hand postures. Wavelengths of the illuminator corresponding to the six spectrum are 460nm, 630nm, 700nm, 850nm, 940nm and white light respectively. Each sample contains six palm images which are captured at the same time with six different electromagnetic spectrums. In each session, there are three samples. The time interval between the two sessions is more than one month. For each hand, we capture two sessions of palm images.
All palm images are 8 bit gray-level JPEG files. CASIA Multi-Spectral Palmprint Image Database contains 7,200 palm images using a self-designed multiple spectral imaging device.
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