578 research outputs found
FARO: FAce Recognition against Occlusions and Expression Variations
Full text linkFARO: FAce Recognition Against Occlusions
and Expression Variations
Maria De Marsico, Member, IEEE, Michele Nappi, and Daniel Riccio
Abstract—Face recognition is widely considered as one of the
most promising biometric techniques, allowing high recognition
rates without being too intrusive. Many approaches have been
presented to solve this special pattern recognition problem, also
addressing the challenging cases of face changes, mainly occurring
in expression, illumination, or pose. On the other hand, less work
can be found in literature that deals with partial occlusions (i.e.,
sunglasses and scarves). This paper presents FAce Recognition
against Occlusions and Expression Variations (FARO) as a new
method based on partitioned iterated function systems (PIFSs),
which is quite robust with respect to expression changes and
partial occlusions. In general, algorithms based on PIFSs compute
a map of self-similarities inside the whole input image, searching
for correspondences among small square regions. However, traditional
algorithms of this kind suffer from local distortions such
as occlusions. To overcome such limitation, information extracted
by PIFS is made local by working independently on each face
component (eyes, nose, and mouth). Distortions introduced by
likely occlusions or expression changes are further reduced by
means of an ad hoc distance measure. In order to experimentally
confirm the robustness of the proposed method to both lighting
and expression variations, as well as to occlusions, FARO has
been tested using AR-Faces database, one of the main benchmarks
for the scientific community in this context. A further validation
of FARO performances is provided by the experimental results
produced on Face Recognition Grand Challenge database
do Espirito Santo Freire, K., Dazzani, M.V, Marsico, G. (2021). A saúde mental de estudantes universitários brasileiros: Uma revisão de literatura integrativa. In G. Gonçalves dos Santos and S. Maria Rocha Sampaio (Ed). Observatorio da Vita Estudantil, Interdisciplinaridade, Vida Estudantil e Diálogo de Saberes. Salvador: Edufba.
No full textO ensino superior é um período importante na vida dos estudantes. Segundo Ressurreição e Sampaio (2018), durante o período universitário o estudante passa por mudanças cognitivas, afetivas e sociais que constituirão a sua trajetória acadêmica e reconfigurarão a sua relação com o mundo. São oportunizadas novas perspectivas de desenvolvimento, envolvendo processos psicológicos interdependentes de processos de aprendizagem, de construção de significados e de reposicionamento de identidad
Biometric 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
A User-oriented Visual Tool for Advanced Editing of Learning Material
No full textWe present AXEL, a WYSIWYG editor for LMML
(Learning Material Markup Language). LMML
augments educational content with meta-information
about its components, according to Passau Teachware
Model (1999). LMML is a complex markup language.
AXEL (Advanced XML Editor for Learning material)
allows any user to easily exploit it. We also enriched
the model with scaffolding components that would
otherwise require non-trivial programming abilities
Gait Recognition: the Wearable Solution
No full textTwo main factors encourage new investigations regarding biometric gait recognition. First, wearable sensors allow a new approach to this problem, which does not suffer from the hindering factors affecting computer vision methods. Occlusions, camera field of view/angle, or illumination are not issues anymore, and it is possible to better focus on gait intrinsic features. Second, wearable sensors are nowadays commonly embedded in widespread mobile devices, especially smartphones. This allows setting up a gait recognition system without special equipment (either cameras or equipped floors). However, even this new recognition approach suffers from specific limitations. Ground slope, shoe heels, walking speed, can cause signal distortions. Their possible effects must be investigated and addressed. The aim of this chapter is to provide the basics to approach gait recognition by mobile wearable sensors, and sketches the most promising techniques, while listing the (few) datasets available at present to test new algorithms
Average effort and average mastery in the identification of the Zone of Proximal Development
No full textWe propose a set of formal definitions aiming to address the personalization and adaptivity of courses in e-learning environments. We build on a developing framework which relies on pedagogical foundations of the Vygotskij Theory, and formalize requirements to meet, in order to support its implementation within e-learning software systems
Automatic Face Image Tagging in Large Collections.
No full textIn this chapter, the authors present some issues related to automatic face image tagging techniques. Their main purpose in user applications is to support the organization (indexing) and retrieval (or easy browsing) of images or videos in large collections. Their core modules include algorithms and strategies for handling very large face databases, mostly acquired in real conditions. As a background for understanding how automatic face tagging works, an overview about face recognition techniques is given, including both traditional approaches and novel proposed techniques for face recognition in uncontrolled settings. Moreover, some applications and the way they work are summarized, in order to depict the state of the art in this area of face recognition research. Actually, many of them are used to tag faces and to organize photo albums with respect to the person(s) presented in annotated photos. This kind of activity has recently expanded from personal devices to social networks, and can also significantly support more demanding tasks, such as automatic handling of large editorial collections for magazine publishing and archiving. Finally, a number of approaches to large-scale face datasets as well as some automatic face image tagging techniques are presented and compared. The authors show that many approaches, both in commercial and research applications, still provide only a semi-automatic solution for this problem
Biometric Systems Evaluation
No full textBiometric system evaluation encompasses
procedures measuring different performance
aspects under objective and quantitative
criteria. Well-defined rules guarantee reliability,
generalizability, and comparability of results. The
evaluation should allow predicting the system
performance over unseen biometric data under
similar, possibly real-world operating conditions.
The guidelines, rules, and protocols for evaluation
activities are continuously updated and
extended. Therefore, this entry is not exhaustive
but rather provides some fundamentals
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