S
Shaokang Chen
Researcher at University of Queensland
Publications - 52
Citations - 1075
Shaokang Chen is an academic researcher from University of Queensland. The author has contributed to research in topics: Facial recognition system & Face detection. The author has an hindex of 14, co-authored 52 publications receiving 1034 citations. Previous affiliations of Shaokang Chen include NICTA.
Papers
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Proceedings ArticleDOI
Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition
TL;DR: An efficient patch-based face image quality assessment algorithm which quantifies the similarity of a face image to a probabilistic face model, representing an ‘ideal’ face is proposed.
Proceedings ArticleDOI
Improved Image Set Classification via Joint Sparse Approximated Nearest Subspaces
TL;DR: In this article, a Frobenius norm distance between subspaces over Grassmann manifolds is defined based on reconstruction error, and the corresponding closest subspace from the samples of a probe image set is constructed by joint sparse representation.
Journal ArticleDOI
Face recognition from still images to video sequences: a local-feature-based framework
TL;DR: The experimental results show that Multi-region Histogram (MRH) feature is more discriminative for face recognition compared to Local Binary Patterns (LBP) and raw pixel intensity and is more suitable for CCTV surveillance systems with constraints on the number of images and the speed of processing.
Journal Article
Improved image set classification via joint sparse approximated nearest subspaces
TL;DR: This work proposes to constrain the clustering of each query image set by forcing the clusters to have resemblance to the clusters in the gallery image sets, and shows that the proposed method consistently outperforms several other recent techniques, such as Affine Hull based Image Set Distance, Sparse Approximated Nearest Points (SANP) and Manifold Discriminant Analysis (MDA).
Proceedings ArticleDOI
Classification of Human Epithelial Type 2 Cell Indirect Immunofluoresence Images via Codebook Based Descriptors
TL;DR: Experiments show that the proposed cell classification system has consistent high performance and is more robust than two recent CAD systems, and is the first time codebook-based descriptors are applied and studied in this domain.