Latent Max-margin Metric Learning for Comparing Video Face Tubes - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2015

Latent Max-margin Metric Learning for Comparing Video Face Tubes

Gaurav Sharma
  • Function : Author
  • PersonId : 972001
Patrick Pérez
  • Function : Author
  • PersonId : 1022281

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.
Fichier principal
Vignette du fichier
sharma_cvprw15.pdf (1.43 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01219829 , version 1 (23-10-2015)

Identifiers

  • HAL Id : hal-01219829 , version 1

Cite

Gaurav Sharma, Patrick Pérez. Latent Max-margin Metric Learning for Comparing Video Face Tubes. Biometrics Workshop, Computer Vision and Pattern Recognition (CVPR), Jun 2015, Boston, United States. ⟨hal-01219829⟩
176 View
79 Download

Share

Gmail Facebook Twitter LinkedIn More