Sciweavers

Share
ICB
2009
Springer

Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference

9 years 6 months ago
Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference
We propose a scalable face matching algorithm capable of dealing with faces subject to several concurrent and uncontrolled factors, such as variations in pose, expression, illumination, resolution, as well as scale and misalignment problems. Each face is described in terms of multi-region probabilistic histograms of visual words, followed by a normalised distance calculation between the histograms of two faces. We also propose a fast histogram approximation method which dramatically reduces the computational burden with minimal impact on discrimination performance. Experiments on the “Labeled Faces in the Wild” dataset (unconstrained environments) as well as FERET (controlled variations) show that the proposed algorithm obtains performance on par with a more complex method and displays a clear advantage over predecessor systems. Furthermore, the use of multiple regions (as opposed to a single overall region) improves accuracy in most cases, especially when dealing with illumination...
Conrad Sanderson, Brian C. Lovell
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where ICB
Authors Conrad Sanderson, Brian C. Lovell
Comments (0)
books