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In this paper, a method to improve texture analysis performance in biometrics by adjusting image sharpness is presented. Images of high sharpness are usually considered as high quality data in texture analysis. Therefore, the imaging sensor and lens are carefully selected and calibrated in an image acquisition system in order to capture clear images. However, the results of our experiments show that the performance of texture analysis in biometrics can be improved by filtering clear images to lower sharpness. The experiments were conducted on the PolyU Palmprint Database using two algorithms (CompCode and POC), as well as on the CASIA Iris Database using IrisCode. In this paper, a filtering method using Gaussian filters is adopted to the images during the pre-processing stage to adjust the image sharpness. Results indicate that there is an optimal range of image sharpness and if all the images are filtered to this range, the performance of texture analysis on the whole dataset will be optimized. A scheme is also proposed to find the optimal range and to filter an image to the optimal range. [ABSTRACT FROM AUTHOR]

Development in low-cost depth acquisition devices pushes the relevant applications forward, which requires higher quality of the depth maps. In this paper, a novel depth de-noising algorithm is proposed, which dithers the depth map according to its spatial and temporal depth classification around the extracted texture edges. After roughly filling the holes in the depth map by joint bilateral filter, the depth map is partitioned into edge region and non-edge region according to the extracted texture edges. The depth values on each side of the mapped texture edges are dithered adapting to depth classification. Joint spatio-temporal dithering approach is proposed to align the depth edges with the texture edges and to suppress the depth artifacts. Experimental results demonstrate that compared with existing depth de-noising approaches, the proposed algorithm provides much sharper and clearer depth edges and it improves the spatial and temporal consistency of the processed depth, which further benefits the applications requiring the depth of high quality, e.g., 3D rendering. [ABSTRACT FROM AUTHOR]

The efficiency of a texture classification procedure depends on the color space in which it is performed. Classification in a perceptually meaningful space requires chromatic coordinates obtained from a calibrated acquisition setup. The authors assess the impact of camera calibration, within a generic color picture acquisition workflow, on the performance of a number of texture classification techniques. An image calibration pipeline is established and applied to a texture database, and the accuracy of the classification algorithms is evaluated for each step. The results show that the most significant step of the workflow is color rendering although the effect is relatively small. Hence precise scene-referred characterization of the raw data from an acquisition camera is not essential for most texture classification tasks. In addition, working with output-referred RGB data is likely to be adequate for the majority of classification tasks. [ABSTRACT FROM AUTHOR]