Face recognition with support vector machines: global versus component-based approach

By Bernd Heisele, Purdy Ho, Tomaso A. Poggio. Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, 2001

554 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b2c48bfbd582e345e93ebdc1afc8d9416369555f

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Citations in the corpus

A review on Gabor wavelets for face recognition (2006, methodology): “A set of SVM classifiers is applied to extract different facial components and the gray values of each component are combined into a single feature vector [2].”
Applications of Support Vector Machines for Pattern Recognition: A Survey (2002, methodology): “Since SVMs are known to generalize well even in high dimensional spaces under small training sample conditions [5] and have shown to be superior to traditional empirical risk minimization principle employed by most of neural networks [6], SVMs have been successfully applied to a number of applicatio”
Human face recognition based on multidimensional PCA and extreme learning machine (2011, methodology): “To enhance the discriminative power of extracted features and to achieve superior face recognition researchers have also proposed the use of Bayesian framework [12,13] and support vector machines (SVM) [14,15].”
Face Recognition: A Literature Review (2008, unknown): “Reference [69] presented a component-based technique and two global techniques for face recognition and evaluated their performance with respect to robustness against pose changes.”
Hierarchical Ensemble of Global and Local Classifiers for Face Recognition (2009, result): “The conflicting results of the two classifiers interestingly reflect the above-mentioned different roles of global and local information, which suggests that ideal classifier should be the combination of the two “experts.””
Robust sparse coding for face recognition (2011, methodology): “Face recognition (FR) is among the most visible and challenging research topics in computer vision and pattern recognition [29], and many methods, such as Eigenfaces [21], Fisherfaces [2] and SVM [7], have been proposed in the past two decades.”
Recent advances in visual and infrared face recognition - a review (2005, methodology): “SVM has applied to face recognition [99–102] and gender classification [103].”
Pedestrian detection and tracking with night vision (2005, background): “recognition such as faces [19], [25], [31], face components [19], and pedestrians [22], [26].”
Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image (2005, background): “There are a number of conventional techniques that have been developed for view-invariant face recognition [4], [6], [10], [12], [13], [15], [25], [26], [28].”
SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results (2010, background): “In this section we will introduce the ﬂat and its equipment and also the notion of Activity of Daily Living and our selected activities to objectivize, in the future, ADLs scale.”
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, methodology): “The “origin” ﬂow pattern inference is performed for the trafﬁc generated from a Wireless Local Area Network (WLAN) in [188] by applying SVMs.”
Learning Hybrid Image Templates (HIT) by Information Projection (2012, methodology): “Inspired by the cortex-like systems [33], [35], [36], we have adopted a recursive SUM-MAX procedure similar to [2], which is described in Algorithm 2.”