S
Stan Z. Li
Researcher at Westlake University
Publications - 625
Citations - 49737
Stan Z. Li is an academic researcher from Westlake University. The author has contributed to research in topics: Facial recognition system & Computer science. The author has an hindex of 97, co-authored 532 publications receiving 41793 citations. Previous affiliations of Stan Z. Li include Microsoft & Macau University of Science and Technology.
Papers
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Book ChapterDOI
Face detection based on multi-block LBP representation
TL;DR: This paper presents the use of a new set of distinctive rectangle features, called Multi-block Local Binary Patterns (MB-LBP), for face detection, which encodes rectangular regions' intensities by local binary pattern operator, and the resulting binary patterns can describe diverse local structures of images.
Proceedings ArticleDOI
Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes
TL;DR: This work proposes a scale invariant local ternary pattern operator and proposes a pattern kernel density estimation technique to effectively model the probability distribution of local patterns in the pixel process, which utilizes only one single LBP-like pattern instead of histogram as feature.
Proceedings ArticleDOI
Face recognition under varying lighting conditions using self quotient image
TL;DR: The theoretical analysis on conditions where the algorithm is applicable and a non-iterative filtering algorithm for computing SQI are presented and experiment results demonstrate the effectiveness of the method for robust face recognition under varying lighting conditions.
Book ChapterDOI
The Visual Object Tracking VOT2014 challenge results
Matej Kristan,Roman Pflugfelder,Ales Leonardis,Jiri Matas,Luka Cehovin,Georg Nebehay,Tomas Vojir,Gustavo Fernandez,Alan Lukezic,Aleksandar Dimitriev,Alfredo Petrosino,Amir Saffari,Bo Li,Bohyung Han,Cherkeng Heng,Christophe Garcia,Dominik Pangersic,Gustav Häger,Fahad Shahbaz Khan,Franci Oven,Horst Possegger,Horst Bischof,Hyeonseob Nam,Jianke Zhu,Jijia Li,Jin-Young Choi,Jin-Woo Choi,João F. Henriques,Joost van de Weijer,Jorge Batista,Karel Lebeda,Kristoffer Öfjäll,Kwang Moo Yi,Lei Qin,Longyin Wen,Mario Edoardo Maresca,Martin Danelljan,Michael Felsberg,Ming-Ming Cheng,Philip H. S. Torr,Qingming Huang,Richard Bowden,Sam Hare,Samantha Yueying Lim,Seunghoon Hong,Shengcai Liao,Simon Hadfield,Stan Z. Li,Stefan Duffner,Stuart Golodetz,Thomas Mauthner,Vibhav Vineet,Weiyao Lin,Yang Li,Yuankai Qi,Zhen Lei,Zhiheng Niu +56 more
TL;DR: The evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset are presented, offering a more systematic comparison of the trackers.
Proceedings ArticleDOI
S^3FD: Single Shot Scale-Invariant Face Detector
TL;DR: S3FD as mentioned in this paper proposes a scale-equitable face detection framework to handle different scales of faces well and improves the recall rate of small faces by a scale compensation anchor matching strategy.