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Titlebook: Biometric Authentication; First International Virginio Cantoni,Dimo Dimov,Massimo Tistarelli Conference proceedings 2014 Springer Internat

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31#
發(fā)表于 2025-3-27 00:40:29 | 只看該作者
“Mother Tongues” of a Multidialectal CityLeast Squares (NLS) function we can compute correctly the position of the two eyes using 6 landmarks for each of them and the pose of the head. Then an active pan-tilt camera is oriented to one of the users eyes. This way a high precision gaze direction determination is accomplished.
32#
發(fā)表于 2025-3-27 02:51:47 | 只看該作者
33#
發(fā)表于 2025-3-27 07:35:41 | 只看該作者
Conference proceedings 2014ntains 5 invited papers. The papers cover a range of topics in the field gait and behaviour analysis; iris analysis; speech recognition; 3D ear recognition; face and facial attributes analysis; handwriting and signature recognition; and multimodal and soft biometrics.
34#
發(fā)表于 2025-3-27 10:56:00 | 只看該作者
35#
發(fā)表于 2025-3-27 16:52:37 | 只看該作者
Curriculum Mapping and Bridging Pedagogies,f behavioral features related to the movements of head, elbows and knees is a very effective tool for gait characterization and people recognition. In particular, our experimental results shows that it is possible to achieve 96% classification accuracy when discriminating a group of 20 people.
36#
發(fā)表于 2025-3-27 19:50:49 | 只看該作者
https://doi.org/10.1007/978-94-007-6476-7hile showing good recognition performance on some of the most referenced public iris dataset, is also able to perform a one-to-one comparison in a small amount of time thanks to its low computing load, thus resulting particularly suited to iris recognition applications on mobile devices.
37#
發(fā)表于 2025-3-27 23:10:51 | 只看該作者
38#
發(fā)表于 2025-3-28 03:25:53 | 只看該作者
39#
發(fā)表于 2025-3-28 07:28:41 | 只看該作者
Human Classification Using Gait Featuresf behavioral features related to the movements of head, elbows and knees is a very effective tool for gait characterization and people recognition. In particular, our experimental results shows that it is possible to achieve 96% classification accuracy when discriminating a group of 20 people.
40#
發(fā)表于 2025-3-28 10:34:20 | 只看該作者
Fast Iris Recognition on Smartphone by means of Spatial Histogramshile showing good recognition performance on some of the most referenced public iris dataset, is also able to perform a one-to-one comparison in a small amount of time thanks to its low computing load, thus resulting particularly suited to iris recognition applications on mobile devices.
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