4 research outputs found
Person Search in Videos with One Portrait Through Visual and Temporal Links
In real-world applications, e.g. law enforcement and video retrieval, one
often needs to search a certain person in long videos with just one portrait.
This is much more challenging than the conventional settings for person
re-identification, as the search may need to be carried out in the environments
different from where the portrait was taken. In this paper, we aim to tackle
this challenge and propose a novel framework, which takes into account the
identity invariance along a tracklet, thus allowing person identities to be
propagated via both the visual and the temporal links. We also develop a novel
scheme called Progressive Propagation via Competitive Consensus, which
significantly improves the reliability of the propagation process. To promote
the study of person search, we construct a large-scale benchmark, which
contains 127K manually annotated tracklets from 192 movies. Experiments show
that our approach remarkably outperforms mainstream person re-id methods,
raising the mAP from 42.16% to 62.27%.Comment: European Conference on Computer Vision (ECCV), 201
WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and
Pedestrian. The challenge focuses on the problem of precise localization of
human faces and bodies, and accurate association of identities. It comprises of
three tracks: (i) WIDER Face which aims at soliciting new approaches to advance
the state-of-the-art in face detection, (ii) WIDER Pedestrian which aims to
find effective and efficient approaches to address the problem of pedestrian
detection in unconstrained environments, and (iii) WIDER Person Search which
presents an exciting challenge of searching persons across 192 movies. In
total, 73 teams made valid submissions to the challenge tracks. We summarize
the winning solutions for all three tracks. and present discussions on open
problems and potential research directions in these topics.Comment: Report of ECCV 2018 workshop: WIDER Face and Pedestrian Challeng
Person Recognition in Personal Photo Collections
People nowadays share large parts of their personal lives through social
media. Being able to automatically recognise people in personal photos may
greatly enhance user convenience by easing photo album organisation. For human
identification task, however, traditional focus of computer vision has been
face recognition and pedestrian re-identification. Person recognition in social
media photos sets new challenges for computer vision, including non-cooperative
subjects (e.g. backward viewpoints, unusual poses) and great changes in
appearance. To tackle this problem, we build a simple person recognition
framework that leverages convnet features from multiple image regions (head,
body, etc.). We propose new recognition scenarios that focus on the time and
appearance gap between training and testing samples. We present an in-depth
analysis of the importance of different features according to time and
viewpoint generalisability. In the process, we verify that our simple approach
achieves the state of the art result on the PIPA benchmark, arguably the
largest social media based benchmark for person recognition to date with
diverse poses, viewpoints, social groups, and events.
Compared the conference version of the paper, this paper additionally
presents (1) analysis of a face recogniser (DeepID2+), (2) new method naeil2
that combines the conference version method naeil and DeepID2+ to achieve state
of the art results even compared to post-conference works, (3) discussion of
related work since the conference version, (4) additional analysis including
the head viewpoint-wise breakdown of performance, and (5) results on the
open-world setup.Comment: 18 pages, 20 figures; to appear in IEEE Transactions on Pattern
Analysis and Machine Intelligenc
A Comprehensive Overview of Biometric Fusion
The performance of a biometric system that relies on a single biometric
modality (e.g., fingerprints only) is often stymied by various factors such as
poor data quality or limited scalability. Multibiometric systems utilize the
principle of fusion to combine information from multiple sources in order to
improve recognition accuracy whilst addressing some of the limitations of
single-biometric systems. The past two decades have witnessed the development
of a large number of biometric fusion schemes. This paper presents an overview
of biometric fusion with specific focus on three questions: what to fuse, when
to fuse, and how to fuse. A comprehensive review of techniques incorporating
ancillary information in the biometric recognition pipeline is also presented.
In this regard, the following topics are discussed: (i) incorporating data
quality in the biometric recognition pipeline; (ii) combining soft biometric
attributes with primary biometric identifiers; (iii) utilizing contextual
information to improve biometric recognition accuracy; and (iv) performing
continuous authentication using ancillary information. In addition, the use of
information fusion principles for presentation attack detection and
multibiometric cryptosystems is also discussed. Finally, some of the research
challenges in biometric fusion are enumerated. The purpose of this article is
to provide readers a comprehensive overview of the role of information fusion
in biometrics.Comment: Accepted for publication in Information Fusio