Learning Structured Ordinal Measures for Video based Face Recognition

TL;DR

This paper introduces a structured ordinal measure approach for video-based face recognition that learns ordinal filters and features, integrating deep representations to improve coding stability and achieve state-of-the-art accuracy.

Contribution

It proposes a novel method combining structured ordinal measures with deep features and an alternating minimization algorithm for improved face recognition in videos.

Findings

Achieves state-of-the-art recognition rates on three face video datasets.

Uses fewer features and samples compared to existing methods.

Demonstrates robustness with both unsupervised and supervised structures.

Abstract

This paper presents a structured ordinal measure method for video-based face recognition that simultaneously learns ordinal filters and structured ordinal features. The problem is posed as a non-convex integer program problem that includes two parts. The first part learns stable ordinal filters to project video data into a large-margin ordinal space. The second seeks self-correcting and discrete codes by balancing the projected data and a rank-one ordinal matrix in a structured low-rank way. Unsupervised and supervised structures are considered for the ordinal matrix. In addition, as a complement to hierarchical structures, deep feature representations are integrated into our method to enhance coding stability. An alternating minimization method is employed to handle the discrete and low-rank constraints, yielding high-quality codes that capture prior structures well. Experimental results on three commonly used face video databases show that our method with a simple voting classifier can achieve state-of-the-art recognition rates using fewer features and samples.