Learning Structured Ordinal Measures for Video based Face Recognition
This addresses face recognition from videos, offering improved efficiency and accuracy, but it is incremental as it builds on existing ordinal and structured methods.
The paper tackles video-based face recognition by proposing a structured ordinal measure method that learns ordinal filters and features, achieving state-of-the-art recognition rates on three databases with fewer features and samples.
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.