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https://www.worldscientific.com/doi/abs/10.1142/S0218001402001575
In order to absorb the resulting high dimensionality of the input space, support vector machines (SVMs), which are known to work well even in high-dimensional space, are used as the face recognizer. Furthermore, a modified form of polynomial kernel (local correlation kernel) is utilized to take account of prior knowledge about facial structures ...Cited by: 30
https://www.researchgate.net/publication/263872847_FACE_RECOGNITION_USING_SUPPORT_VECTOR_MACHINES_WITH_LOCAL_CORRELATION_KERNELS
face recognition using support vector machines with local correlation kernels Article in International Journal of Pattern Recognition and Artificial Intelligence 16(01) · …
https://researchportal.bath.ac.uk/en/publications/face-recognition-using-support-vector-machines-with-local-correla
title = "Face recognition using support vector machines with local correlation kernels", abstract = "This paper presents a real-time face recognition system. For the system to be real time, no external time-consuming feature extraction method is used, rather the gray-level values of the raw pixels that make up the face pattern are fed directly to the recognizer.Cited by: 30
https://www.researchgate.net/publication/283311937_Face_Recognition_Using_RBF_Kernel_Based_Support_Vector_Machine
This paper introduces a novel face recognition using support vector machines with the robust feature extracted by kernel principal component analysis (KPCA), which is robust to facial variations.
https://ieeexplore.ieee.org/document/1251793/
This paper introduces a novel face recognition using support vector machines with the robust feature extracted by kernel principal component analysis (KPCA), which is robust to facial variations.
https://pdfs.semanticscholar.org/8c66/1e613d96210a02e5c7faad6d000b7d683e26.pdf
Support vector machine (SVM) learning is a recent technology that gives a decent broad view performance this paper given the most recent algorithms developed for face recognition and tries to give an idea of the state of the art of face recognition technology. And mention some advantages and
http://cbcl.mit.edu/publications/ps/iccv2001.pdf
methods for face recognition and evaluate them with re-spect to robustness againstpose changes. In the component system we first locate facial components, extract them and combine them into a single feature vector which is classi-fied by a Support Vector Machine (SVM). The two global systems recognize faces by classifying a single feature vec-
https://papers.nips.cc/paper/1609-support-vector-machines-applied-to-face-recognition.pdf
Support Vector Machines Applied to Face Recognition 805 SVM can be extended to nonlinear decision surfaces by using a kernel K ( " .) that satisfies Mercer's condition [1, 7]. The nonlinear decision surface is L Ns oWiK(sj, z) + b = O. i= l A facial image is represented as a vector P E RN, where RN is referred to as face space.
https://www.sciencedirect.com/science/article/pii/S0925231216305665
Robust face detection using local CNN and SVM based on kernel combination. ... we propose a locality-sensitive support vector machine using kernel combination (LS-KC-SVM) algorithm to solve the above two problems. First, we employ the locality-sensitive SVM (LSSVM) to construct a local model on each local region, which can handle the ...Cited by: 25
https://www.hindawi.com/journals/cmmm/2012/205025/
Correlation Kernel has been recently applied to classification problems with Support Vector Machines (SVMs). In this paper, we develop a novel and parsimonious positive semidefinite kernel. The proposed kernel is shown experimentally to have better performance when compared to the usual correlation kernel.Cited by: 11
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