3,937 research outputs found
Modeling feature distances by orientation driven classifiers for person re-identification
6siTo tackle the re-identification challenges existing methods propose to directly match image features or to learn the transformation of features that undergoes between two cameras. Other methods learn optimal similarity measures. However, the performance of all these methods are strongly dependent from the person pose and orientation. We focus on this aspect and introduce three main contributions to the field: (i) to propose a method to extract multiple frames of the same person with different orientations in order to capture the complete person appearance; (ii) to learn the pairwise feature dissimilarities space (PFDS) formed by the subspaces of similar and different image pair orientations; and (iii) within each subspace, a classifier is trained to capture the multi-modal inter-camera transformation of pairwise image dissimilarities and to discriminate between positive and negative pairs. The experiments show the superior performance of the proposed approach with respect to state-of-the-art methods using two publicly available benchmark datasets. © 2016 Elsevier Inc. All rights reserved.partially_openopenGarcía, Jorge; Martinel, Niki; Gardel, Alfredo; Bravo, Ignacio; Foresti, Gian Luca; Micheloni, ChristianGarcía, Jorge; Martinel, Niki; Gardel, Alfredo; Bravo, Ignacio; Foresti, Gian Luca; Micheloni, Christia
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
Pigment Melanin: Pattern for Iris Recognition
Recognition of iris based on Visible Light (VL) imaging is a difficult
problem because of the light reflection from the cornea. Nonetheless, pigment
melanin provides a rich feature source in VL, unavailable in Near-Infrared
(NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical
not stimulated in NIR. In this case, a plausible solution to observe such
patterns may be provided by an adaptive procedure using a variational technique
on the image histogram. To describe the patterns, a shape analysis method is
used to derive feature-code for each subject. An important question is how much
the melanin patterns, extracted from VL, are independent of iris texture in
NIR. With this question in mind, the present investigation proposes fusion of
features extracted from NIR and VL to boost the recognition performance. We
have collected our own database (UTIRIS) consisting of both NIR and VL images
of 158 eyes of 79 individuals. This investigation demonstrates that the
proposed algorithm is highly sensitive to the patterns of cromophores and
improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on
Instruments and Measurements, Volume 59, Issue number 4, April 201
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Evaluating soft biometrics in the context of face recognition
2013 Summer.Includes bibliographical references.Soft biometrics typically refer to attributes of people such as their gender, the shape of their head, the color of their hair, etc. There is growing interest in soft biometrics as a means of improving automated face recognition since they hold the promise of significantly reducing recognition errors, in part by ruling out illogical choices. Here four experiments quantify performance gains on a difficult face recognition task when standard face recognition algorithms are augmented using information associated with soft biometrics. These experiments include a best-case analysis using perfect knowledge of gender and race, support vector machine-based soft biometric classifiers, face shape expressed through an active shape model, and finally appearance information from the image region directly surrounding the face. All four experiments indicate small improvements may be made when soft biometrics augment an existing algorithm. However, in all cases, the gains were modest. In the context of face recognition, empirical evidence suggests that significant gains using soft biometrics are hard to come by
- …