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PCM
2007
Springer
169views Multimedia» more  PCM 2007»
14 years 2 months ago
Random Subspace Two-Dimensional PCA for Face Recognition
The two-dimensional Principal Component Analysis (2DPCA) is a robust method in face recognition. Much recent research shows that the 2DPCA is more reliable than the well-known PCA ...
Nam Nguyen, Wanquan Liu, Svetha Venkatesh
ICMCS
2005
IEEE
94views Multimedia» more  ICMCS 2005»
14 years 2 months ago
Using partial information for face recognition and pose estimation
The main achievement of this work is the development of a new face recognition approach called Partial Principal Component Analysis (P2 CA), which exploits the novel concept of us...
Antonio Rama, Francesc Tarres, Davide Onofrio, Ste...
AVBPA
2001
Springer
157views Biometrics» more  AVBPA 2001»
14 years 1 months ago
EigenGait: Motion-Based Recognition of People Using Image Self-Similarity
We present a novel technique for motion-based recognition of individual gaits in monocular sequences. Recent work has suggested that the image self-similarity plot of a moving per...
Chiraz BenAbdelkader, Ross Cutler, Harsh Nanda, La...
DAGM
2006
Springer
14 years 9 days ago
On-Line, Incremental Learning of a Robust Active Shape Model
Abstract. Active Shape Models are commonly used to recognize and locate different aspects of known rigid objects. However, they require an off-line learning stage, such that the ex...
Michael Fussenegger, Peter M. Roth, Horst Bischof,...
ICPR
2010
IEEE
13 years 10 months ago
Compressing Sparse Feature Vectors Using Random Ortho-Projections
In this paper we investigate the usage of random ortho-projections in the compression of sparse feature vectors. The study is carried out by evaluating the compressed features in ...
Esa Rahtu, Mikko Salo, Janne Heikkilä