# Robust Bayesian Method for Simultaneous Block Sparse Signal Recovery with Applications to Face Recognition

arXiv: https://arxiv.org/abs/1605.02057 · HTML: https://mlsanity.com/paper/1605.02057/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2016.

## Built on by (most cited first, 1 of 1)

- [3D Facial Imperfection Regeneration: Deep learning approach and 3D printing prototypes](https://mlsanity.com/paper/2303.14381/) (2023)

## Abstract (arXiv)

In this paper, we present a novel Bayesian approach to recover simultaneously block sparse signals in the presence of outliers. The key advantage of our proposed method is the ability to handle non-stationary outliers, i.e. outliers which have time varying support. We validate our approach with empirical results showing the superiority of the proposed method over competing approaches in synthetic data experiments as well as the multiple measurement face recognition problem.