This paper presents a new approach to improve the performance of finger-vein identification systems presented in the literature. The proposed system simultaneously acquires the finger-vein and low-resolution fingerprint images and combines these two evidences using a novel score-level combination strategy. We examine the previously proposed finger-vein identification approaches and develop a new approach that illustrates it superiority over prior published efforts. The utility of low-resolution fingerprint images acquired from a webcam is examined to ascertain the matching performance from such images. We develop and investigate two new score-level combinations, i.e., holistic and nonlinear fusion, and comparatively evaluate them with more popular score-level fusion approaches to ascertain their effectiveness in the proposed system. The rigorous experimental results presented on the database of 6264 images from 156 subjects illustrate significant improvement in the performance, i.e., both from the authentication and recognition experiments.

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Vision-based inspection of industrial materials such as textile webs, paper, or wood requires the development of defect segmentation techniques based on texture analysis. In this work, a multi-channel filtering technique that imitates the early human vision process is applied to images captured on-line. This new approach uses Bernoulli's rule of combination for integrating images from different channels. Physical image size and yam impurities are used as key parameters for tuning the sensitivity of the proposed algorithm. Several real fabric samples along with the result of segmented defects are presented. The results achieved show that the developed algorithm is robust, scalable and computationally efficient for detection of local defects in textured materials.

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