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» A Hierarchical Face Recognition Algorithm
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PR
2008
87views more  PR 2008»
13 years 8 months ago
Two-dimensional Laplacianfaces method for face recognition
In this paper we propose a two-dimensional (2D) Laplacianfaces method for face recognition. The new algorithm is developed based on two techniques, i.e., locality preserved embedd...
Ben Niu, Qiang Yang, Simon Chi-Keung Shiu, Sankar ...
CVPR
2005
IEEE
14 years 11 months ago
Representational Oriented Component Analysis (ROCA) for Face Recognition with One Sample Image per Training Class
Subspace methods such as PCA, LDA, ICA have become a standard tool to perform visual learning and recognition. In this paper we propose Representational Oriented Component Analysi...
Fernando De la Torre, Ralph Gross, Simon Baker, B....
ICIAR
2010
Springer
14 years 1 months ago
Adaptation of SIFT Features for Robust Face Recognition
Abstract. The Scale Invariant Feature Transform (SIFT) is an algorithm used to detect and describe scale-, translation- and rotation-invariant local features in images. The origina...
Janez Krizaj, Vitomir Struc, Nikola Pavesic
AAAI
2008
13 years 11 months ago
Economic Hierarchical Q-Learning
Hierarchical state decompositions address the curse-ofdimensionality in Q-learning methods for reinforcement learning (RL) but can suffer from suboptimality. In addressing this, w...
Erik G. Schultink, Ruggiero Cavallo, David C. Park...
PR
2006
113views more  PR 2006»
13 years 8 months ago
Face recognition from a single image per person: A survey
One of the main challenges faced by the current face recognition techniques lies in the difficulties of collecting samples. Fewer samples per person mean less laborious effort for...
Xiaoyang Tan, Songcan Chen, Zhi-Hua Zhou, Fuyan Zh...