Learning Structured Ordinal Measures for Video based Face Recognition
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Face RecognitionSimilar Papers 제목 키워드 기반
COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition
Automatically recognising apparent emotions from face and voice is hard, in part because of various sources of uncertainty, including in the input data and the labels used in a machine learning framework. This paper intr…
Emotion RecognitionMultimodal Emotion RecognitionEvaluating Evaluation Measures for Ordinal Classification and Ordinal Quantification
Ordinal Classification (OC) is an important classification task where the classes are ordinal. For example, an OC task for sentiment analysis could have the following classes: highly positive, positive, neutral, negative…
ClassificationOrdinal ClassificationSentiment AnalysisBridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos
Action recognition models have shown a promising capability to classify human actions in short video clips. In a real scenario, multiple correlated human actions commonly occur in particular orders, forming semantically …
Action SegmentationAction UnderstandingActivity RecognitionHuman Activity RecognitionAleatoric and Epistemic Uncertainty Measures for Ordinal Classification through Binary Reduction
Ordinal classification problems, where labels exhibit a natural order, are prevalent in high-stakes fields such as medicine and finance. Accurate uncertainty quantification, including the decomposition into aleatoric (in…
Ordinal ClassificationBayesian InferenceOrdinalBench: A Benchmark Dataset for Diagnosing Generalization Limits in Ordinal Number Understanding of Vision-Language Models
Vision-Language Models (VLMs) have advanced across multimodal benchmarks but still show clear gaps in ordinal number understanding, i.e., the ability to track relative positions and generalize to large indices. We presen…