J
Jeng-Shyang Pan
Researcher at Shandong University of Science and Technology
Publications - 889
Citations - 14887
Jeng-Shyang Pan is an academic researcher from Shandong University of Science and Technology. The author has contributed to research in topics: Digital watermarking & Computer science. The author has an hindex of 50, co-authored 789 publications receiving 11645 citations. Previous affiliations of Jeng-Shyang Pan include National Kaohsiung Normal University & Technical University of Ostrava.
Papers
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Book ChapterDOI
Statistical Learning-Based Face Recognition
TL;DR: A comprehensive survey on face recognition from practical applications, sensory inputs, methods, and application conditions, and a comprehensive survey of face recognition methods from the viewpoints of signal processing and machine learning are implemented.
Journal ArticleDOI
The evolutionary random interval fingerprint for a more secure wireless communication
TL;DR: A novel evolutionary Random Interval Fingerprint RIF for active RFID and ZigBee systems that is flexible in generating uniform random and long cycle numbers, and more robust for the anti-cracking.
Proceedings ArticleDOI
High capacity watermark embedding based on local invariant features
TL;DR: A novel robust image watermarking scheme is presented to embed a high capacity watermark into the feature point based characteristic regions and it can efficiently resist traditional signal processing attacks and geometric attacks.
Book ChapterDOI
Spatial-Based Watermarking Schemes and Pixel Selection
TL;DR: This chapter discusses spatial domain based watermarking schemes, which possess the advantages, such as easy implementation and low complexity, but also posses the shortages like weak robustness and non-practical usage.
Proceedings ArticleDOI
Dimensionality reduction based on nonparametric discriminant analysis with kernels for feature extraction and recognition
TL;DR: Experimental results on ORL, YALE and UMIST face databases show that NKDA outperforms NDA on recognition, which demonstrates that it is feasible to improve NDA with kernel trick for feature extraction.