On the Use of SIFT Features for Face Authentication

By Manuele Bicego, Andrea Lagorio, Enrico Grosso, Massimo Tistarelli. 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06), 2006

409 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/786d681c0694c686e7defbce26170e2995f568b0

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. S. Li
2. Xiaogang Wang
3. Anil K. Jain
4. Yi Yang
5. Luc Van Gool
6. Dacheng Tao
7. Shengcai Liao
8. Yunhong Wang

Citations in the corpus

• Partial Face Recognition: Alignment-Free Approach (2013, background): “A number of papers have been published on SIFT-based face recognition [45], [46], [47], [48].”
• Recognizing Disguised Faces: Human and Machine Evaluation (2014, methodology): “…Principal Component Analysis (PCA) [2], Fisher’s Linear Discriminant Analysis (LDA) [3], Independent Component Analysis (ICA) [4], Elastic Bunch Graph Matching (EBGM) [5], Local Binary Patterns (LBP) [6], Scale Invariant Feature Transform (SIFT) [7], and Sparse Representation Classifier (SRC) [8].”
• Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition (2009, background): “Person-specific image-based approaches are recently introduced with promising results for face recognition (Ma et al. 2007; Bicego et al. 2006; Ahonen et al. 2006).”
• Scale Invariant Feature Transform (2012, methodology): “…recognition, these types of methods can also be used for related tasks such as visual search in image databases (Lew et al. 2006, Datta et al. 2008), human computer interaction based on visual input (Porta 2002, Jaimesa and Sebe 2008) or biometrics (Bicego et al. 2006, Li 2009, Wang et al. 2010).”
• Oriented Response Networks (2017, methodology): “With dominant orientation based feature alignment, SIFT achieves invariance to rotation and robustness to moderate perspective transforms [2, 12].”
• Local Binary Patterns and Its Application to Facial Image Analysis: A Survey (2011, positive): “On the other hand, the local-based ones, e.g. [63] [101] [152], proceed firstly to locate a number of features or components from a face; then classify them by combining and comparing corresponding local statistics.”