The Experts below are selected from a list of 5547 Experts worldwide ranked by ideXlab platform

Cheng Xiang - One of the best experts on this subject based on the ideXlab platform.

  • recursive Fisher Linear Discriminant for bci applications
    International Conference on Intelligent Sensors Sensor Networks and Information, 2007
    Co-Authors: D Huang, Cheng Xiang
    Abstract:

    A novel recursive procedure for extracting Discriminant features, termed Recursive Fisher Linear Discriminant (RFLD), is applied to brain-computer interface (BCI) problems. Compared to traditional Fisher Linear Discriminant (FLD), RFLD relaxes the constraint on the total number of features that can be extracted. The new RFLD has been tested on motor imagery classification with the electrocorticography (ECoG) signals. The resulting improvement of performance by the new feature extraction scheme suggests the effectiveness of our method.

  • a novel lda algorithm based on approximate error probability with application to face recognition
    International Conference on Image Processing, 2006
    Co-Authors: D Huang, Cheng Xiang
    Abstract:

    Extracting proper features is crucial to the performance of a pattern recognition system. Popular feature extraction techniques like principal component analysis (PCA), Fisher Linear Discriminant analysis (FLD), and independent component analysis (ICA) extract features that are not directly related to the classification accuracy. In this paper, we propose a new Linear Discriminant analysis algorithm (LDA) whose criterion function is based on the probability of classification error. The efficiency of this novel algorithm is demonstrated by application to face recognition problems.

  • face recognition using recursive Fisher Linear Discriminant
    IEEE Transactions on Image Processing, 2006
    Co-Authors: Cheng Xiang, X A Fan, Tong Heng Lee
    Abstract:

    Fisher Linear Discriminant (FLD) has recently emerged as a more efficient approach for extracting features for many pattern classification problems as compared to traditional principal component analysis. However, the constraint on the total number of features available from FLD has seriously limited its application to a large class of problems. In order to overcome this disadvantage, a recursive procedure of calculating the Discriminant features is suggested in this paper. The new algorithm incorporates the same fundamental idea behind FLD of seeking the projection that best separates the data corresponding to different classes, while in contrast to FLD the number of features that may be derived is independent of the number of the classes to be recognized. Extensive experiments of comparing the new algorithm with the traditional approaches have been carried out on face recognition problem with the Yale database, in which the resulting improvement of the performances by the new feature extraction scheme is significant

  • face recognition using recursive Fisher Linear Discriminant with gabor wavelet coding
    International Conference on Image Processing, 2004
    Co-Authors: Cheng Xiang, X A Fan, Tong Heng Lee
    Abstract:

    The constraint on the total number of features available from the Fisher Linear Discriminant (FLD) has seriously limited its application to a large class of problems. In order to overcome this disadvantage of FLD, a recursive procedure for calculating the Discriminant features is suggested in this paper. Extensive experiments of comparing the new algorithm with the traditional PCA and FLD approaches have been carried out on a face recognition problem, in which the resulting improvement of the performance by the new feature extraction scheme is significant.

  • face recognition using recursive Fisher Linear Discriminant
    International Conference on Communications Circuits and Systems, 2004
    Co-Authors: Cheng Xiang, X A Fan, Tong Heng Lee
    Abstract:

    The Fisher Linear Discriminant (FLD) has recently emerged as a more efficient approach for extracting features for many pattern classification problems than traditional principal component analysis (PCA). However, the constraint on the total number of features available from FLD has seriously limited its application to a large class of problems. In order to overcome this disadvantage of FLD, a recursive procedure for calculating the Discriminant features is suggested in this paper. Extensive experiments of comparing the new algorithm with the traditional PCA and FLD approaches have been carried out on a face recognition problem, in which the resulting improvement of the performance by the new feature extraction scheme is significant.

Qingmin Liao - One of the best experts on this subject based on the ideXlab platform.

  • weighted contourlet binary patterns and image based Fisher Linear Discriminant for face recognition
    Neurocomputing, 2017
    Co-Authors: Yichuan Wang, Yinyan Jiang, Qingmin Liao
    Abstract:

    Abstract We propose a novel face representation model, called the weighted Contourlet binary patterns (WCBP), based on the NonSubsampled Contourlet Transform (NSCT), for face recognition. The decomposition using NSCT can capture rich image information at multiple scales, orientations, and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each NSCT-decomposed image. We also propose to carry out a subsequent Fisher Linear Discriminant (FLD) on each decomposed image (named as WCBP+FLD) for dimension reduction of features. Our extensive experiments on the public FERET, CAS-PEAL-R1 and LFW databases demonstrate that the non-weighted Contourlet binary patterns performs better than local Gabor binary patterns. WCBP further improves the recognition rates. WCBP+FLD can achieve much competitive or even better recognition performance compared with the state-of-the-art Gabor feature based face recognition methods.

  • face recognition based on nonsubsampled contourlet transform and block based kernel Fisher Linear Discriminant
    International Conference on Acoustics Speech and Signal Processing, 2012
    Co-Authors: Biao Wang, Qingmin Liao
    Abstract:

    Face representation, including both feature extraction and feature selection, is the key issue for a successful face recognition system. In this paper, we propose a novel face representation scheme based on nonsubsampled contourlet transform (NSCT) and block-based kernel Fisher Linear Discriminant (BKFLD). NSCT is a newly developed multiresolution analysis tool and has the ability to extract both intrinsic geometrical structure and directional information in images, which implies its discriminative potential for effective feature extraction of face images. By encoding the the NSCT coefficient images with the local binary pattern (LBP) operator, we could obtain a robust feature set. Furthermore, kernel Fisher Linear Discriminant is introduced to select the most discriminative feature sets, and the block-based scheme is incorporated to address the small sample size problem. Face recognition experiments on FERET database demonstrate the effectiveness of our proposed approach.

Harry Wechsler - One of the best experts on this subject based on the ideXlab platform.

  • gabor feature based classification using the enhanced Fisher Linear Discriminant model for face recognition
    IEEE Transactions on Image Processing, 2002
    Co-Authors: Harry Wechsler
    Abstract:

    This paper introduces a novel Gabor-Fisher (1936) classifier (GFC) for face recognition. The GFC method, which is robust to changes in illumination and facial expression, applies the enhanced Fisher Linear Discriminant model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images. The novelty of this paper comes from (1) the derivation of an augmented Gabor feature vector, whose dimensionality is further reduced using the EFM by considering both data compression and recognition (generalization) performance; (2) the development of a Gabor-Fisher classifier for multi-class problems; and (3) extensive performance evaluation studies. In particular, we performed comparative studies of different similarity measures applied to various classifiers. We also performed comparative experimental studies of various face recognition schemes, including our novel GFC method, the Gabor wavelet method, the eigenfaces method, the Fisherfaces method, the EFM method, the combination of Gabor and the eigenfaces method, and the combination of Gabor and the Fisherfaces method. The feasibility of the new GFC method has been successfully tested on face recognition using 600 FERET frontal face images corresponding to 200 subjects, which were acquired under variable illumination and facial expressions. The novel GFC method achieves 100% accuracy on face recognition using only 62 features.

  • enhanced Fisher Linear Discriminant models for face recognition
    International Conference on Pattern Recognition, 1998
    Co-Authors: Chengjun Liu, Harry Wechsler
    Abstract:

    We introduce two enhanced Fisher Linear Discriminant (FLD) models (EFM) in order to improve the generalization ability of the standard FLD based classifiers such as Fisherfaces. Similar to Fisherfaces, both EFM models apply first principal component analysis (PCA) for dimensionality reduction before proceeding with FLD type of analysis. EFM-1 implements the dimensionality reduction with the goal to balance between the need that the selected eigenvalues account for most of the spectral energy of the raw data and the requirement that the eigenvalues of the within-class scatter matrix in the reduced PCA subspace are not too small. EFM-2 implements the dimensionality reduction as Fisherfaces do. It proceeds with the whitening of the within-class scatter matrix in the reduced PCA subspace and then chooses a small set of features (corresponding to the eigenvectors of the within-class scatter matrix) so that the smaller trailing eigenvalues are not included in further computation of the between-class scatter matrix. Experimental data using a large set of faces-1,107 images drawn from 369 subjects and including duplicates acquired at a later time under different illumination-from the FERET database shows that the EFM models outperform the standard FLD based methods.

Tong Heng Lee - One of the best experts on this subject based on the ideXlab platform.

  • face recognition using recursive Fisher Linear Discriminant
    IEEE Transactions on Image Processing, 2006
    Co-Authors: Cheng Xiang, X A Fan, Tong Heng Lee
    Abstract:

    Fisher Linear Discriminant (FLD) has recently emerged as a more efficient approach for extracting features for many pattern classification problems as compared to traditional principal component analysis. However, the constraint on the total number of features available from FLD has seriously limited its application to a large class of problems. In order to overcome this disadvantage, a recursive procedure of calculating the Discriminant features is suggested in this paper. The new algorithm incorporates the same fundamental idea behind FLD of seeking the projection that best separates the data corresponding to different classes, while in contrast to FLD the number of features that may be derived is independent of the number of the classes to be recognized. Extensive experiments of comparing the new algorithm with the traditional approaches have been carried out on face recognition problem with the Yale database, in which the resulting improvement of the performances by the new feature extraction scheme is significant

  • face recognition using recursive Fisher Linear Discriminant with gabor wavelet coding
    International Conference on Image Processing, 2004
    Co-Authors: Cheng Xiang, X A Fan, Tong Heng Lee
    Abstract:

    The constraint on the total number of features available from the Fisher Linear Discriminant (FLD) has seriously limited its application to a large class of problems. In order to overcome this disadvantage of FLD, a recursive procedure for calculating the Discriminant features is suggested in this paper. Extensive experiments of comparing the new algorithm with the traditional PCA and FLD approaches have been carried out on a face recognition problem, in which the resulting improvement of the performance by the new feature extraction scheme is significant.

  • face recognition using recursive Fisher Linear Discriminant
    International Conference on Communications Circuits and Systems, 2004
    Co-Authors: Cheng Xiang, X A Fan, Tong Heng Lee
    Abstract:

    The Fisher Linear Discriminant (FLD) has recently emerged as a more efficient approach for extracting features for many pattern classification problems than traditional principal component analysis (PCA). However, the constraint on the total number of features available from FLD has seriously limited its application to a large class of problems. In order to overcome this disadvantage of FLD, a recursive procedure for calculating the Discriminant features is suggested in this paper. Extensive experiments of comparing the new algorithm with the traditional PCA and FLD approaches have been carried out on a face recognition problem, in which the resulting improvement of the performance by the new feature extraction scheme is significant.

Xin Yang - One of the best experts on this subject based on the ideXlab platform.

  • face recognition using enhanced Fisher Linear Discriminant model with facial combined feature
    Pacific Rim International Conference on Artificial Intelligence, 2004
    Co-Authors: Dake Zhou, Xin Yang
    Abstract:

    Achieving higher classification rate under various conditions is a challenging problem in face recognition community. This paper presents a combined feature Fisher classifier (CF2C) approach for face recognition, which is robust to moderate changes of illumination, pose and facial expression. The success of this method lies in that it uses both facial global and local information for robust face representation while at the same time employs an enhanced Fisher Linear Discriminant model (EFM) for good generalization. Experiments on ORL and Yale face databases show that the proposed approach is superior to traditional methods, such as eigenfaces and Fisherfaces.