Stan Z. Li

TL;DR: A set of orthogonal, binary, localized basis components are learned from a well-aligned face image database and leads to a Walsh function-based representation of the face images, which can be used to resolve the occlusion problem, improve the computing efficiency and compress the storage requirements of a face detection and recognition system.

TL;DR: A novel hierarchical selecting scheme embedded in linear discriminant analysis (LDA) and AdaBoost learning is proposed to select the most effective and most robust features and to construct a strong classifier for face recognition systems.

TL;DR: It is demonstrated that ICA, TICA, and ISA are able to learn view-specific basis components unsupervisedly from the mixture data and thereby explain underlying reasons for the emergent formation of view subspaces.

TL;DR: It is shown that 2D-LDA has eliminated the information contained in the covariance information between different local geometric structures, which is useful for discriminant feature extraction, whereas 1D- LDA could preserve such information, and this new finding indicates that 1D -LDA is able to gain higher Fisher score than 2D/LDA in some extreme case.

TL;DR: The proposed multi-label convolutional neural network (MLCNN) can simultaneously predict multiple pedestrian attributes and significantly outperforms the SVM based method on the PETA database.