NDLI: Latent max-margin metric learning for comparing video face tubes

Content Provider	IEEE Xplore Digital Library
Author	Sharma, G. Perez, P.
Copyright Year	2015
Description	Author affiliation: MPI Inf., Germany (Sharma, G.) \|\| Technicolor, Paris, France (Perez, P.)
Abstract	Comparing “face tubes” is a key component of modern systems for face biometrics based video analysis and annotation. We present a novel algorithm to learn a distance metric between such spatio-temporal face tubes in videos. The main novelty in the algorithm is based on incorporation of latent variables in a max-margin metric learning framework. The latent formulation allows us to model, and learn metrics to compare faces under different challenging variations in pose, expressions and lighting. We propose a novel dataset named TV Series Face Tubes (TSFT) for evaluating the task. The dataset is collected from 12 different episodes of 8 popular TV series and has 94 subjects with 569 manually annotated face tracks in total. We show quantitatively how incorporating latent variables in max-margin metric learning leads to improvement of current state-of-the-art metric learning methods for the two cases when the testing is done with subjects that were seen during training and when the test subjects were not seen at all during training. We also give results on a challenging benchmark dataset: YouTube faces, and place our algorithm in context w.r.t. existing methods.
Starting Page	65
Ending Page	74
File Size	1309663
Page Count	10
File Format	PDF
ISSN	21607516
e-ISBN	9781467367592
DOI	10.1109/CVPRW.2015.7301321
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Lighting Estimation Electron tubes
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Latent Max-margin Metric Learning for Comparing Video Face Tubes

Latent Max-margin Metric Learning for Comparing Video Face Tubes

Margin Emphasized Metric Learning and its application to Gabor feature based face recognition

Environment coupled metrics learning for unconstrained face verification

Efficient Max-Margin Metric Learning

Efficient max-margin metric learning.

Margin emphasized metric learning and its application to gabor feature based face recognition.

Unsupervised metric learning for face identification in TV video

Max-margin nonparametric latent feature models for link prediction.

Latent max-margin metric learning for comparing video face tubes

Similar Documents

Latent Max-margin Metric Learning for Comparing Video Face Tubes

Latent Max-margin Metric Learning for Comparing Video Face Tubes

Margin Emphasized Metric Learning and its application to Gabor feature based face recognition

Environment coupled metrics learning for unconstrained face verification

Efficient Max-Margin Metric Learning

Efficient max-margin metric learning.

Margin emphasized metric learning and its application to gabor feature based face recognition.

Unsupervised metric learning for face identification in TV video

Max-margin nonparametric latent feature models for link prediction.

Latent max-margin metric learning for comparing video face tubes