14,193 research outputs found
Face recognition technologies for evidential evaluation of video traces
Human recognition from video traces is an important task in forensic investigations and evidence evaluations. Compared with other biometric traits, face is one of the most popularly used modalities for human recognition due to the fact that its collection is non-intrusive and requires less cooperation from the subjects. Moreover, face images taken at a long distance can still provide reasonable resolution, while most biometric modalities, such as iris and fingerprint, do not have this merit. In this chapter, we discuss automatic face recognition technologies for evidential evaluations of video traces. We first introduce the general concepts in both forensic and automatic face recognition , then analyse the difficulties in face recognition from videos . We summarise and categorise the approaches for handling different uncontrollable factors in difficult recognition conditions. Finally we discuss some challenges and trends in face recognition research in both forensics and biometrics . Given its merits tested in many deployed systems and great potential in other emerging applications, considerable research and development efforts are expected to be devoted in face recognition in the near future
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
Mutimodal Ranking Optimization for Heterogeneous Face Re-identification
Heterogeneous face re-identification, namely matching heterogeneous faces
across disjoint visible light (VIS) and near-infrared (NIR) cameras, has become
an important problem in video surveillance application. However, the large
domain discrepancy between heterogeneous NIR-VIS faces makes the performance of
face re-identification degraded dramatically. To solve this problem, a
multimodal fusion ranking optimization algorithm for heterogeneous face
re-identification is proposed in this paper. Firstly, we design a heterogeneous
face translation network to obtain multimodal face pairs, including
NIR-VIS/NIR-NIR/VIS-VIS face pairs, through mutual transformation between
NIR-VIS faces. Secondly, we propose linear and non-linear fusion strategies to
aggregate initial ranking lists of multimodal face pairs and acquire the
optimized re-ranked list based on modal complementarity. The experimental
results show that the proposed multimodal fusion ranking optimization algorithm
can effectively utilize the complementarity and outperforms some relative
methods on the SCface dataset
- …