The analysis of authorial style, termed stylometry, assumes that style is quantifiably measurable for evaluation of distinctive qualities. Stylometry research has yielded several methods and tools over the past 200 years to handle a variety of challenging cases. This survey reviews several articles within five prominent subtasks: authorship attribution, authorship verification, authorship profiling, stylochronometry, and adversarial stylometry. Discussions on datasets, features, experimental techniques, and recent approaches are provided. Further, a current research challenge lies in the inability of authorship analysis techniques to scale to a large number of authors with few text samples. Here, we perform an extensive performance analysis on a corpus of 1,000 authors to investigate authorship attribution, verification, and clustering using 14 algorithms from the literature. Finally, several remaining research challenges are discussed, along with descriptions of various open-source and commercial software that may be useful for stylometry subtasks.

In the recent past, deep learning methods have demonstrated remarkable success for supervised learning tasks in multiple domains including computer vision, natural language processing, and speech processing. In this article, we investigate the impact of deep learning in the field of biometrics, given its success in other domains. Since biometrics deals with identifying people by using their characteristics, it primarily involves supervised learning and can leverage the success of deep learning in other related domains. In this article, we survey 100 different approaches that explore deep learning for recognizing individuals using various biometric modalities. We find that most deep learning research in biometrics has been focused on face and speaker recognition. Based on inferences from these approaches, we discuss how deep learning methods can benefit the field of biometrics and the potential gaps that deep learning approaches need to address for real-world biometric applications.

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Short-term memory, verbal fluency, and positive affect in middle-old age may contribute to resilience against online spear-phishing attacks. These results inform mechanisms of online fraud susceptibility and real-life decision-supportive interventions towards fraud risk reduction in aging.

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Fitting an ellipse to the iris boundaries accounts for the projective distortions present in off-axis images of the eye and provides the contour fitting necessary for the dimensionless mapping used in leading iris recognition algorithms. Previous iris segmentation efforts have either focused on fitting circles to pupillary and limbic boundaries or assigning labels to image pixels. This paper approaches the iris segmentation problem by adapting the Starburst algorithm to locate pupillary and limbic feature pixels used to fit a pair of ellipses. The approach is evaluated by comparing the fits to ground truth. Two metrics are used in the evaluation, the first based on the algebraic distance between ellipses, the second based on ellipse chamfer images. Results are compared to segmentations produced by NDIRIS over randomly selected images from the Iris Challenge Evaluation database. Statistical evidence shows significant improvement of Starburst's elliptical fits over the circular fits on which NDIRIS relies. I. INTRODUCTIONExcept for several relatively unique approaches, e.g., [3], [16], common iris segmentation methods model the iris as a pair of circles [5]. Although the inner and outer boundaries of the iris may be roughly approximated by circles, they rarely appear as true circles in images [9]. The iris image is subject to perspective projection. It is approximately planar. Any circle that lies in a plane not fronto-parallel to the camera will appear elliptical in the image plane. The segmentation model must account for such distortions. A general ellipse model is therefore more appropriate than a restricted circular model to compensate for this type of distortion.The Starburst algorithm was introduced by Li, Babcock, and Parkhurst for the purpose of eye tracking [14]. For such an application, Starburst's main objective is to identify feature points on the limbus for subsequent localization of the pupil center. Starburst then fits an ellipse to the limbic pixels, operating under the implicit assumption that the center of that ellipse coincides with the pupil center. The pupil center is then used for estimating the point of gaze, or POG, of a viewer wearing the eye tracking apparatus.In this paper we adapt the Starburst algorithm for the purpose of iris segmentation. The novelty behind our adaptation is the simultaneous identification of both pupillary and limbic boundaries, fitting ellipses to both contours, thereby producing an iris segmentation suitable for subsequent iris recognition as well as eye tracking applications. Such contour fitting is an essential component of iris recognition [8].