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» Learning Representative Local Features for Face Detection
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89
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CVPR
2001
IEEE
16 years 8 days ago
Learning Representative Local Features for Face Detection
This paper describes a face detection approach via learning local features. The key idea is that local features, being manifested by a collection of pixels in a local region, are ...
Xiangrong Chen, Lie Gu, Stan Z. Li, HongJiang Zhan...
98
Voted
FGR
2008
IEEE
208views Biometrics» more  FGR 2008»
15 years 4 months ago
Unsupervised learning from local features for video-based face recognition
This paper presents an unsupervised learning approach to video-based face recognition that does not make any assumptions about the pose, expressions or prior localization of landm...
Ajmal Mian
CVPR
2004
IEEE
16 years 8 days ago
Learning Object Detection from a Small Number of Examples: The Importance of Good Features
Face detection systems have recently achieved high detection rates[11, 8, 5] and real-time performance[11]. However, these methods usually rely on a huge training database (around...
Kobi Levi, Yair Weiss
98
Voted
CVPR
2001
IEEE
16 years 8 days ago
Learning Probabilistic Distribution Model for Multi-View Face Detection
Modeling subspaces of a distribution of interest in high dimensional spaces is a challenging problem in pattern analysis. In this paper, we present a novel framework for pose inva...
Lie Gu, Stan Z. Li, HongJiang Zhang
98
Voted
ICIAR
2010
Springer
15 years 3 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