# Sparse Representation-based Open Set Recognition

## TL;DR

This paper introduces a novel open set recognition method based on sparse representation and extreme value theory, effectively identifying unknown classes by modeling error distribution tails, outperforming existing algorithms.

## Contribution

It develops a generalized SRC algorithm that models tail errors with EVT for improved open set recognition, a novel approach not previously explored.

## Key findings

- Significantly outperforms existing open set recognition methods.
- Effective modeling of error distribution tails improves unknown class detection.
- Validated on four public datasets with strong results.

## Abstract

We propose a generalized Sparse Representation- based Classification (SRC) algorithm for open set recognition where not all classes presented during testing are known during training. The SRC algorithm uses class reconstruction errors for classification. As most of the discriminative information for open set recognition is hidden in the tail part of the matched and sum of non-matched reconstruction error distributions, we model the tail of those two error distributions using the statistical Extreme Value Theory (EVT). Then we simplify the open set recognition problem into a set of hypothesis testing problems. The confidence scores corresponding to the tail distributions of a novel test sample are then fused to determine its identity. The effectiveness of the proposed method is demonstrated using four publicly available image and object classification datasets and it is shown that this method can perform significantly better than many competitive open set recognition algorithms. Code is public available: https://github.com/hezhangsprinter/SROSR