Semi-Supervised Sparse Representation Based Classification for Face Recognition with Insufficient Labeled Samples

TL;DR

This paper introduces S$^3$RC, a semi-supervised sparse representation method for face recognition that effectively handles limited and corrupted labeled samples by modeling linear and non-linear nuisance variables.

Contribution

The paper proposes a novel semi-supervised sparse representation classification approach that combines linear nuisance modeling with prototype estimation using GMM, improving recognition with scarce labeled data.

Findings

Significantly improved recognition accuracy on multiple face databases.

Effective handling of both linear and non-linear nuisance variables.

Robust performance even with only a single labeled sample per person.

Abstract

This paper addresses the problem of face recognition when there is only few, or even only a single, labeled examples of the face that we wish to recognize. Moreover, these examples are typically corrupted by nuisance variables, both linear (i.e., additive nuisance variables such as bad lighting, wearing of glasses) and non-linear (i.e., non-additive pixel-wise nuisance variables such as expression changes). The small number of labeled examples means that it is hard to remove these nuisance variables between the training and testing faces to obtain good recognition performance. To address the problem we propose a method called Semi-Supervised Sparse Representation based Classification (S$^3$RC). This is based on recent work on sparsity where faces are represented in terms of two dictionaries: a gallery dictionary consisting of one or more examples of each person, and a variation dictionary representing linear nuisance variables (e.g., different lighting conditions, different glasses). The main idea is that (i) we use the variation dictionary to characterize the linear nuisance variables via the sparsity framework, then (ii) prototype face images are estimated as a gallery dictionary via a Gaussian Mixture Model (GMM), with mixed labeled and unlabeled samples in a semi-supervised manner, to deal with the non-linear nuisance variations between labeled and unlabeled samples. We have done experiments with insufficient labeled samples, even when there is only a single labeled sample per person. Our results on the AR, Multi-PIE, CAS-PEAL, and LFW databases demonstrate that the proposed method is able to deliver significantly improved performance over existing methods.