Recent studies show fusion at level of segmentation to be useful for more robust iris recognition rates compared with simple segmentation. In this paper we perform Sum-Rule Interpolation at level of the result of the normalized segmented iris images using the well-known Daugman's algorithm, since the process of normalization is essentially composed by two parts: Iris segmentation, in which the pupillary and limbic polar curves are detected and Iris normalization: a normalized representation of the iris texture is created using angular and pupil-tolimbic radial coordinates. For evaluation we propose an experimental fusion scheme using three automatic segmentation algorithms which have reported good results and are not computationally expensive. The experiments were performed on the CASIA V3-Interval, CASIA.V4-Thousand and UBIRIS V1 datasets showing increased recognition accuracy for representative feature extraction algorithms.
The reconstruction of electrophysiological sources within the brain is sensitive to the constructed head model, which depends on the positioning accuracy of anatomical landmarks known as fiducials. In this work, we propose an algorithm for the automatic detection of fiducial landmarks of EEG electrodes on the 3D human head model. Our proposal combines a dimensional reduction approach with a perspective projection from 3D to 2D object space; the eye and ear automatic detection in a 2D face image by two cascades of classifiers and geometric transformations to obtain 3D spatial coordinates of the landmarks and to generate the head coordinate system, This is accomplished by considering the characteristics of the scanner information. Capturing the 3D model of the head is done with Occipital Inc. ST01 structure sensor and the implementation of our algorithm was carried out on MATLAB R2018b using the Computer Vision Toolbox and the FieldTrip Toolbox. The experimental results were aimed at recursively exploring the efficacy of the facial feature detectors as a function of the projection angle; they show that robust results are obtained in terms of false acceptance rate. Our proposal is an initial step of an approach for the automatic digitization of electrode locations. The experimental results demonstrate that the proposed method detects anatomical facial landmarks automatically, accurately, and rapidly.
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