Boosting Local Binary Pattern (LBP)-Based Face Recognition

By Guangcheng Zhang, Xiangsheng Huang, S. Li, Yangsheng Wang, Xihong Wu. Chinese Conference on Biometric Recognition, 2004

302 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/52818433a95c7dc357a6348b458255e06c1b00b2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Xiaogang Wang
2. Yi Yang
3. Luc Van Gool
4. Dacheng Tao
5. S. Li
6. Yu Qiao
7. Andrew Zisserman
8. Zhen Lei

Citations in the corpus

• Learning Multi-scale Block Local Binary Patterns for Face Recognition (2007, methodology): “Zhang et al [17] propose to use AdaBoost learning to select best LBP sub-window histograms features and construct face classifiers.”
• A Survey of Recent Advances in Face Detection (2010, background): “Another well-known feature set robust to illumination variations is the local binary patterns (LBP) [65], which have been very effective for face recognition tasks [2, 117].”
• Facial expression recognition based on Local Binary Patterns: A comprehensive study (2009, methodology): “In [54], Zhang et al. presented an approach for face recognition by boosting LBP-based classifiers, where the distance between corresponding LBP histograms of two face images is used as a discriminative feature, and AdaBoost was used to learn a few of most efficient features.”
• Boosted multi-task learning for face verification with applications to web image and video search (2009, methodology): “It also outperforms the similarly trained AdaBoost classifier based on LBP histograms of local regions [22].”
• Learning local binary patterns for gender classification on real-world face images (2012, methodology): “In Zhang et al. (2004), Sun et al. (2006), Adaboost was used to learn the discriminative sub-regions (in term of LBP histogram) from a large pool of sub-regions generated by shifting and scaling a sub-window over face images.”
• On the Use of SIFT Features for Face Authentication (2006, methodology): “For simplicity, given a keypoint k i , let’s call F ( k i ) , L ( k i ) , S ( k i ) and O ( k i ) its feature descriptor, its location, its scale and its orientation, respectively.”
• A Survey of Face Recognition Techniques (2009, background): “Recently, Zhang et al. [159] proposed an approach in which a similarity function is learned describing the level of confidence that two images belong to the same person, similar to [88].”
• A survey on face detection in the wild: Past, present and future (2015, background): “Another well-known feature set robust to illumination variations is the local binary patterns (LBP) [20], which have been very effective for face recognition tasks [21] [70].”