A joint discriminative generative model for deformable model construction and classification
File(s)
Accepted version
Author(s)
Marras, Ioannis
Nikitidis, Symeon
Zafeiriou, Stefanos
Pantic, Maja
Type
Conference Paper
Abstract
Discriminative classification models have been successfully applied for various computer vision tasks such as object and face detection and recognition. However, deformations can change objects coordinate space and perturb robust similarity measurement, which is the essence of all classification algorithms. The common approach to deal with deformations is either to seek for deformation invariant features or to develop models that describe objects deformations. However, the former approach requires a huge amount of data and a good amount of engineering to be properly trained, while the latter require considerable human effort in the form of carefully annotated data. In this paper, we propose a method that jointly learns with minimal human intervention a generative deformable model using only a simple shape model of the object and images automatically downloaded from the Internet, and also extracts features appropriate for classification. The proposed algorithm is applied on various classification problems such as “in-the-wild” face recognition, gender classification and eye glasses detection on data retrieved by querying into a web image search engine. We demonstrate that not only it outperforms other automatic methods by large margins, but also performs comparably with supervised methods trained on thousands of manually annotated data.
Date Issued
2017-06-29
Date Acceptance
2017-05-30
Citation
Automatic Face & Gesture Recognition (FG 2017), 2017 12th IEEE International Conference on, 2017, pp.127-134
ISSN
2326-5396
Publisher
IEEE
Start Page
127
End Page
134
Journal / Book Title
Automatic Face & Gesture Recognition (FG 2017), 2017 12th IEEE International Conference on
Copyright Statement
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (E
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000414287400017&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
EP/J017787/1
EP/N007743/1
Source
12th IEEE International Conference on Automatic Face and Gesture Recognition (FG)
Subjects
ACTIVE APPEARANCE MODELS
SUPPORT VECTOR MACHINES
FACE RECOGNITION
POSE
Publication Status
Published
Start Date
2017-05-30
Finish Date
2017-06-03
Coverage Spatial
Washington, DC