Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image

By Tae-Kyun Kim, Josef Kittler. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005

313 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4a64db5a2238bd4f3e3314529c6bec688b9453e6

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. Dacheng Tao
2. Luc Van Gool
3. G. Wang
4. Jiwen Lu
5. Ming-Hsuan Yang
6. Xiaogang Wang
7. Ling Shao
8. Yi Yang

Citations in the corpus

• Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition (2009, methodology): “The idea is extended by Kim to LDA sp aces with soft clustering idea in 2005 (Kim and Kittler 2005).”
• Evaluating Complexity, Code Churn, and Developer Activity Metrics as Indicators of Software Vulnerabilities (2011, background): “Discriminant analysis has widespread applications, including facial recognition [21, 37].”
• Probabilistic Linear Discriminant Analysis for Inferences About Identity (2007, background): “STUDY DATABASE POSE DIFF( o ) % Gross [9] FERET (100) 30( Ave. ) 75 Blanz [2] FRVT (87) 45 86 Kim [10] XM2VTS (125) 30 53 Prince [14] FERET (100) 90 86 Our Approach XM2VTS (100) 90 87 Table 2: Results for % correct face identification across large pose changes.”
• Discriminative multi-manifold analysis for face recognition from a single training sample per person (2011, methodology): “For example, Kim and Kittler [20] employed a generic training set to learn discriminative features to address the SSPP problem in pose-invariant face recognition.”
• Geometric Mean for Subspace Selection (2009, background): “…the linear dimensionality reduction step in FLDA has a critical drawback: for a classification task with c classes, if the dimension of the projected subspace is strictly lower than c 1, the projection to a subspace tends to merge those classes, which are close together in the original…”
• Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations (2007, methodology): “The methods based on LDA have been widely advocated in the literature [7], [9], [29], [30], [35], [18].”
• Eigenfeature Regularization and Extraction in Face Recognition (2008, background): “The agreeable properties of the linear subspace analysis and its promising performance achieved in the face recognition encourage researchers to extend it to higher order statistics [10], [11], nonlinear methods [12], [13], [14], Gabor features [15], [16], and locality preserving projections [ 17 ],”