Face and Expression Recognition
This cluster of papers focuses on the application of various machine learning and dimensionality reduction techniques to the field of face recognition. It covers topics such as feature selection, support vector machines, ensemble methods, local binary patterns, non-negative matrix factorization, spectral clustering, Laplacian eigenmaps, and sparse representation in the context of face recognition.
Papers listed on taxonomy pages are the top few works per node from the OpenAlex snapshot. That list is not exhaustive and is not an endorsement. The topic map and the journal registry remain separate: there is still no authoritative topic-to-venue or topic-to-organization edge. Search is a lexical lookup, not a claim that a venue publishes a topic.
Most cited
- A Threshold Selection Method from Gray-Level Histograms
- LIBSVM
- Regularization Paths for Generalized Linear Models via Coordinate Descent
- Nonlinear Dimensionality Reduction by Locally Linear Embedding
- Robust Real-Time Face Detection
- Eigenfaces for Recognition
Most recent
- Similarity and approaching direction based substitutability analysis method for k-nearest neighbor classifier
- Group-size adaptive kernel extension for feature upsampling
- AN ENHANCED DEEP LEARNING FRAMEWORK FOR ROBUST LICENSE PLATE DETECTION AND RECOGNITION UNDER CHALLENGING REAL-WORLD CONDITIONS
- ENHANCED UNSUPERVISED FEATURE SELECTION USING BINARY BAT ALGORITHM
- Robust multi-view subspace clustering with enhanced high-order correlations
- Incremental linear discriminant analysis with ordered weighted average operator for feature extraction