Latent max-margin metric learning for comparing video face tubes

  title={Latent max-margin metric learning for comparing video face tubes},
  author={Gaurav Sharma and Patrick Perez},
  journal={2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
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… CONTINUE READING
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