1,721,152 research outputs found
Face Recognition in Adverse Conditions: A Look at Achieved Advancements
No full textIn this chapter, the authors discuss the main outcomes from both the most recent literature and the research activities summarized in this book. Of course, a complete review is not possible. It is evident that each issue related to face recognition in adverse conditions can be considered as a research topic in itself and would deserve a detailed survey of its own. However, it is interesting to provide a compass to orient one in the presently achieved results in order to identify open problems and promising research lines. In particular, the final chapter provides more detailed considerations about possible future developments.</jats:p
ES-RU: an entropy based rule to select representative templates in face surveillance
No full textES-RU is a system for video sequence indexing. Video frames are annotated according to the identities of appearing subjects. The system architecture is designed by distributing the different processing steps across dedicated modules. These modules interact with each other to accomplish the final task. Such modularity is also designed to allow a high system flexibility, because it is possible to independently substitute each component with a different one performing the same task using a different method. As an example, face detection is presently performed by Viola–Jones algorithm, but the corresponding module might be substituted by one exploiting neural networks or support vector machines (which are actually more computationally demanding). In detail, ES-RU implements both face location and analysis, and an algorithm to select the most representative templates for the selected identities. The novelty of the algorithm for template analysis and selection relies on the proposed use of the concept of entropy. This concept is the base of most techniques that exploit relative entropy to estimate the degree of uniqueness which is assured by a biometric trait, when processed by a Feature Extraction Technique (FET). In this paper, entropy is introduced as a tool to evaluate the contribution of each sample in guaranteeing a suitable diversification of the templates that make up the gallery of a relevant subject. Video-surveillance activities cause to gather a huge amount of templates to be used for tracking and re-identifying subjects. However, most of these templates are not informative enough to be useful. The aim of our approach is to provide an effective technique to keep only the most “representative” of them, i.e. those that provide a sufficient level of diversification. This allows faster processing (less comparisons) and better results (it is possible to recognize a subject under different conditions). ES-RU was tested on six video clips and on a subset of the SCFace database to assess its performances
Entropy based Biometric Template Clustering
No full textThough speed and accuracy are two competing requirements for large scale biometric recognition, they both suffer from large database size. Clustering seems promising to reduce the search space. This can improve accuracy, but may even contrarily affect it by a poor selection of the candidate cluster for the search. We present a novel technique that exploits gallery entropy for clustering. The comparison with K-Means demonstrates that we achieve a better clustering result, yet without fixing the number of clusters a-priori
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
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
Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods 2018
No full textProceedings of the 7th International Conference on Pattern Recognition Applications and Methods 201
Special issue on novel insights on ocular biometrics
No full textOcular biometrics have a great potential to support biometric applications,
due to the unique features of the ocular traits. Notwithstanding
this, the related lines of research still present several open issues,which
justify the ongoing research efforts. For instance, the relatively recent
emergence of the periocular and sclera traits makes it worth recording
the progresses in those areas. Furthermore,wider and deeper investigations
regarding all the traits underlying the ocular region and the best
way to combine them still needs to be thoroughly undertaken. This
would not only improve the recognition robustness, but alsomake perceiving
the potential of this kind of solutions in solving problems in the
biometrics domain. Moreover, “systems interpretability”, “weakly/partial
supervised recognition” or “forensics evidence and biometric recognition”
add interest to an already rich field of research. This special issue
aims at providing a platform to publish and record the recent research
on ocular biometrics in order to push the state-of-the-art forward
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