A highly scalable incremental facial feature extraction method

Authors:
Fengxi Song;Hang Liu;David Zhang;Jingyu Yang
Affiliations:
New Star Research Institute of Applied Technology in Hefei City, Automation and Simulation, No. 451 Huangshan Road, Hefei, Anhui 230031, China;New Star Research Institute of Applied Technology in Hefei City, Automation and Simulation, No. 451 Huangshan Road, Hefei, Anhui 230031, China;New Star Research Institute of Applied Technology in Hefei City, Automation and Simulation, No. 451 Huangshan Road, Hefei, Anhui 230031, China;New Star Research Institute of Applied Technology in Hefei City, Automation and Simulation, No. 451 Huangshan Road, Hefei, Anhui 230031, China
Venue:
Neurocomputing
Year:
2008

Citing 15
Cited 2

Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Using Discriminant Eigenfeatures for Image Retrieval

IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
An eigenspace update algorithm for image analysis

Graphical Models and Image Processing
Candid Covariance-Free Incremental Principal Component Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised Tensor Learning

ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Human Carrying Status in Visual Surveillance

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Rapid and brief communication: Two-dimensional discriminant transform for face recognition

Pattern Recognition
2D-LDA: A statistical linear discriminant analysis for image matrix

Pattern Recognition Letters
Incremental linear discriminant analysis for classification of data streams

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multilinear Discriminant Analysis for Face Recognition

IEEE Transactions on Image Processing
Face recognition by independent component analysis

IEEE Transactions on Neural Networks

Face recognition in the presence of age differences using holistic and subpattern-based approaches

WAV'09 Proceedings of the 3rd WSEAS international symposium on Wavelets theory and applications in applied mathematics, signal processing & modern science
Distributed Multi-Feature Recognition Scheme for Greyscale Images

Neural Processing Letters

Quantified Score

Hi-index	0.01

Visualization

Abstract

Face recognition is one of the most challenging tasks in biometrics, machine vision, and pattern recognition. Methods that can dynamically extract facial features and perform online classification are especially important for real-world applications. The potentially most useful methods in these cases would include incremental learning techniques such as Incremental Principal Component Analysis (IPCA) and Incremental Discriminant Analysis (ILDA). In this paper, we propose a novel incremental facial feature extraction method-Incremental Weighted Average Samples (IWAS). The new method is very simple in theory and experimental results conducted on two benchmark face image databases demonstrate that it is more effective and efficient than IPCA and ILDA, making IWAS especially applicable to real-time face recognition.