T
Tieniu Tan
Researcher at Chinese Academy of Sciences
Publications - 727
Citations - 46303
Tieniu Tan is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Feature extraction & Iris recognition. The author has an hindex of 96, co-authored 704 publications receiving 39487 citations. Previous affiliations of Tieniu Tan include Association for Computing Machinery & Center for Excellence in Education.
Papers
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Book ChapterDOI
Learning effective intrinsic features to boost 3d-based face recognition
TL;DR: In this paper, a novel representation, called intrinsic features, is presented to encode local 3D shapes and complementary non-relational features are used to provide an intrinsic representation of faces.
Proceedings ArticleDOI
Articulated model based people tracking using motion models
TL;DR: This paper focuses on acquisition of human motion data such as joint angles and velocity for applications of virtual reality, using both an articulated body model and a motion model in the CONDENSATION framework, and proposes a PEF (pose evaluation function) modeled with a radial term.
Journal ArticleDOI
MAPNet: Multi-modal attentive pooling network for RGB-D indoor scene classification
TL;DR: A novel unified framework named Multi-modal Attentive Pooling Network (MAPNet) is proposed, which helps to understand mechanisms of both scene classification and multi- modal fusion in MAPNet and is interpretable.
Proceedings ArticleDOI
Boosting local feature descriptors for automatic objects classification in traffic scene surveillance
TL;DR: This paper proposes a new strategy for object classification by boosting different local feature descriptors in motion blobs by evaluating the performance of each local feature descriptor, but also fuse these descriptors to achieve better performance.
Proceedings ArticleDOI
Robust nose detection in 3D facial data using local characteristics
TL;DR: This paper proposes a robust scheme to solve a specific problem, i.e. locating the nose lip and nose ridge using the local statistic features and included angle curve, which is very significant to 3D face modelling, recognition and registration.