976 research outputs found
A study of aggregated 2D Gabor features on appearance-based face recognition
Author name used in this publication: Wai-Kin KongAuthor name used in this publication: David ZhangRefereed conference paper2004-2005 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe
Face Recognition: Issues, Methods and Alternative Applications
Face recognition, as one of the most successful applications of image analysis, has recently gained significant attention. It is due to availability of feasible technologies, including mobile solutions. Research in automatic face recognition has been conducted since the 1960s, but the problem is still largely unsolved. Last decade has provided significant progress in this area owing to advances in face modelling and analysis techniques. Although systems have been developed for face detection and tracking, reliable face recognition still offers a great challenge to computer vision and pattern recognition researchers. There are several reasons for recent increased interest in face recognition, including rising public concern for security, the need for identity verification in the digital world, face analysis and modelling techniques in multimedia data management and computer entertainment. In this chapter, we have discussed face recognition processing, including major components such as face detection, tracking, alignment and feature extraction, and it points out the technical challenges of building a face recognition system. We focus on the importance of the most successful solutions available so far. The final part of the chapter describes chosen face recognition methods and applications and their potential use in areas not related to face recognition
Facial Expression Analysis under Partial Occlusion: A Survey
Automatic machine-based Facial Expression Analysis (FEA) has made substantial
progress in the past few decades driven by its importance for applications in
psychology, security, health, entertainment and human computer interaction. The
vast majority of completed FEA studies are based on non-occluded faces
collected in a controlled laboratory environment. Automatic expression
recognition tolerant to partial occlusion remains less understood, particularly
in real-world scenarios. In recent years, efforts investigating techniques to
handle partial occlusion for FEA have seen an increase. The context is right
for a comprehensive perspective of these developments and the state of the art
from this perspective. This survey provides such a comprehensive review of
recent advances in dataset creation, algorithm development, and investigations
of the effects of occlusion critical for robust performance in FEA systems. It
outlines existing challenges in overcoming partial occlusion and discusses
possible opportunities in advancing the technology. To the best of our
knowledge, it is the first FEA survey dedicated to occlusion and aimed at
promoting better informed and benchmarked future work.Comment: Authors pre-print of the article accepted for publication in ACM
Computing Surveys (accepted on 02-Nov-2017
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
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