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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Journal ArticleDOI
A system for learning statistical motion patterns
TL;DR: Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.
Journal ArticleDOI
A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs
TL;DR: Experimental results show that this first work based on deep CNNs for gait recognition in the literature outperforms the previous state-of-the-art methods by a significant margin, and shows great potential for practical applications.
Posted Content
Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval
TL;DR: In this work, deep convolutional neural network is incorporated into hash functions to jointly learn feature representations and mappings from them to hash codes, which avoids the limitation of semantic representation power of hand-crafted features.
Journal ArticleDOI
Brief review of invariant texture analysis methods
Jianguo Zhang,Tieniu Tan +1 more
TL;DR: This paper considers invariant texture analysis, and approaches whose performances are not affected by translation, rotation, affine, and perspective transform are addressed.
Proceedings ArticleDOI
Ordinal palmprint represention for personal identification [represention read representation]
TL;DR: A novel palmprint representation - ordinal measure is presented, which unifies several major existing palmprint algorithms into a general framework and achieves higher accuracy, with the equal error rate reduced by 42% for a difficult set, while the complexity of feature extraction is halved.