1,720,965 research outputs found
Guest editorial introduction to the special executable issue on "Mobile Iris Challenge Evaluation part i (MICHE I)"
No full textBiometric walk recognizer: Gait recognition by a single smartphone accelerometer
No full textThis paper presents an approach to gait recognition based on a single consumer accelerometer, built in most present mobile devices. It does not propose a completely novel
algorithm, but rather investigates better ways to exploit the Dynamic TimeWarping (DTW), which is still one of the most used at present in literature. To this aim, the paper presents both a new segmentation algorithm to split the gait signal into cycles/steps, and investigates the best way to use the possibly segmented signal for recognition. Summarizing, the first contribution of the present work is the proposal of a new segmentation algorithm for the gait signal, which does not require any pre-processing, either interpolation or noise reduction, to enhance the original signal, and its comparison with two other state-of-the-art step segmentation algorithms. The second contribution is related to the extensive tests performed with the five different investigated matching methods. The tests are carried out exploiting all compared segmentation algorithms and three different datasets, collected using different sensors: the originally exploited BWR dataset, that includes walk templates from 30
volunteers, and two huge datasets used for this kind of testing, namely the ZJU-gaitacc and the OU-ISIR Inertial Sensor Database. Tests have been performed in both verification
mode, either single-template or multiple-template, and identification mode, both closed and open set. The latter is rarely found in literature though representing the most frequently predictable applicative setting. It is worth underlining that the final goal is to allow using low-cost, built-in sensors that nowadays equip most smartphones. The best result in closed set identification, which is the identification mode usually reported in literature, is achieved using the most constrained method, i.e., limiting the walks in the gallery and in the probe to have a similar number of steps. It reaches ≈ 93 % of Recognition Rate (RR) on ZJUgaitacc dataset. The best result obtained with methods exploiting segmentation to overcome the mentioned limitation reaches ≈ 83 % of Recognition Rate (RR) on the same dataset, using our proposed algorithm. The best results in verification is achieved using multiple templates per user, again without segmentation, with an Equal Error Rate (EER) of 0.09, while the best results with segmentation is achieved again with our algorithm and is and EER of 0.10. This is a very good result for a soft biometrics as gait if often considered. As expected, open set identification achieves lower performance
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Leveraging implicit demographic information for face recognition using a multi-expert system
No full textThis paper describes a novel biometric architecture to implement unsupervised face recognition across varying demographics. The present proposal deals with ethnicity, gender and age, but the same strategy can be crafted for any mix of soft/hard biometrics, sensors, and/or methods. Our aim is not to explicitly distinguish demographic features of a subject (e.g., male vs. female). We rather aim at implicitly exploiting such information to improve the accuracy of subject identification. The role demographics plays in authentication has been reported by many recent studies. Exploiting demographic information can entail two possible strategies. Both require pre-determination of relevant demographic classes, that drive the choice of the best suited recognizer in a set of ad-hoc trained ones. In the first strategy, a human operator visually classifies demographic features of the subject to recognize, and runs the appropriate “strong” recognizer. In the second one, the identification of the most appropriate “strong” recognizer follows the results obtained from a set of upstream classifiers for soft biometrics. Both solutions are poorly suited to most real world applications, e.g., video - surveillance. Our architecture mediates recognition across different demographics without any pre-determination of demographic features. We still have different “strong” classifiers, each trained on a demographic class. The probe is submitted to all of them at once. A supervisor module estimates reliability of the single responses, and the most reliable result is returned. In this approach, classifier reliability is not a static feature, but it is estimated for each probe. The proposed multiple-expert system provides similar performance to pre-determination of demographics. Experimental results show higher flexibility, efficacy and interoperability. We also focus on interoperability across face datasets by adopting EGA (Ethnicity, Gender and Age) database as a benchmark, which is obtained by combining images from several publicly available face datasets
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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