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FClassNet: a fingerprint classification network integrated with the domain knowledge
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Learning discriminative and invariant representation for fingerprint retrieval
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Robust sparse representation based face recognition in an adaptive weighted spatial pyramid structure
The sparse representation based classification methods has achieved significant performance in recent years. To fully exploit both the holistic and locality information of face samples, a series of sparse repr...
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Collaborative representation Bayesian face recognition
**年来, 基于协同表示的人脸识别方法取得了诸多进展。然而, 在训练集字典欠完备的小样本情况下, 基于协同表示的分类方法的结果并不理想。这很大程度上, 是由于该方法使用了欧式距离残差判据。传统的欧式距离残差判据在小样本情况下, 并不能有效地区分类内残差和类间残差。针对这一问题, 我们引入了贝叶斯残差模型来更好地区分类内残差, 并将协同表示机制与贝叶斯残差模型结合, 提出了协同表示贝叶斯人脸识别方法。实验证明, 我们提...
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Open AccessOn the translation-invariance of image distance metric
An appropriate choice of the distance metric is a fundamental problem in pattern recognition, machine learning and cluster analysis. Some methods that based on the distance of samples, e.g, the k-means clustering...
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High-resolution palmprint minutiae extraction based on Gabor feature
Extracting effective minutiae is difficult for high-resolution palmprint, because of the strong influence from principal lines, creases, and other noises. In this paper, a novel minutiae detection and reliabil...
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Downsampling sparse representation and discriminant information aided occluded face recognition
In this paper, a strategy is proposed to deal with a challenging research topic, occluded face recognition. Our approach relies on sparse representation on downsampled input image to first locate unoccluded fa...
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Erratum to: Sparse Representation Shape Models
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Sparse Representation Shape Models
It is well-known that, during shape extraction, enrolling an appropriate shape constraint model could effectively improve locating accuracy. In this paper, a novel deformable shape model, Sparse Representation...
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Further results on the margin explanation of boosting: new algorithm and experiments
Understanding the empirical success of boosting algorithms is an important theoretical problem in machine learning. One of the most influential works is the margin theory, which provides a series of upper boun...
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Boosting and margin theory
Many researchers have worked on the explanation of AdaBoost’s good experimental results in theory. Some work give an upper bound of generalization error in terms of the margin distribution function, while Brei...
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A Novel Margin Based Algorithm for Feature Extraction
Margin based feature extraction has become a hot topic in machine learning and pattern recognition. In this paper, we present a novel feature extraction method called Adaptive Margin Maximization (AMM) in whic...
