Results 1 - 10
of
80
Gabor-based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2004
"... This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial lo ..."
Abstract
-
Cited by 88 (3 self)
- Add to MetaCart
This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semi-definite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semi-definite Gram matrix, either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across 5 poses (left and right profiles, left and right half profiles, and frontal view) with 2 different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gaborbased Chengjun Liu is with the Department of Computer Science, New J...
Dominant Local Binary Patterns for Texture Classification
"... Abstract—This paper proposes a novel approach to extract image features for texture classification. The proposed features are robust to image rotation, less sensitive to histogram equalization and noise. It comprises of two sets of features: dominant local binary patterns (DLBP) in a texture image a ..."
Abstract
-
Cited by 49 (1 self)
- Add to MetaCart
(Show Context)
Abstract—This paper proposes a novel approach to extract image features for texture classification. The proposed features are robust to image rotation, less sensitive to histogram equalization and noise. It comprises of two sets of features: dominant local binary patterns (DLBP) in a texture image and the supplementary features extracted by using the circularly symmetric Gabor filter responses. The dominant local binary pattern method makes use of the most frequently occurred patterns to capture descriptive textural information, while the Gabor-based features aim at supplying additional global textural information to the DLBP features. Through experiments, the proposed approach has been intensively evaluated by applying a large number of classification tests to histogram-equalized, randomly rotated and noise corrupted images in Outex, Brodatz, Meastex, and CUReT texture image databases. Our method has also been compared with six published texture features in the experiments. It is experimentally demonstrated that the proposed method achieves the highest classification accuracy in various texture databases and image conditions. Index Terms—Circularly symmetric Gabor filter, local binary pattern, rotation invariance, texture classification. I.
Uncorrelated multilinear discriminant analysis with regularization and aggregation for tensor object recognition
- IEEE Trans. Neural Netw
, 2009
"... This paper proposes a novel uncorrelated multilinear discriminant analysis (UMLDA) algorithm for the challenging problem of gait recognition. A tensor-to-vector projection (TVP) of tensor objects is formulated and the UMLDA is developed using TVP to extract uncorrelated discriminative features direc ..."
Abstract
-
Cited by 20 (12 self)
- Add to MetaCart
(Show Context)
This paper proposes a novel uncorrelated multilinear discriminant analysis (UMLDA) algorithm for the challenging problem of gait recognition. A tensor-to-vector projection (TVP) of tensor objects is formulated and the UMLDA is developed using TVP to extract uncorrelated discriminative features directly from tensorial data. The small-sample-size (SSS) problem present when discriminant solutions are applied to the problem of gait recognition is discussed and a regularization procedure is introduced to address it. The effectiveness of the proposed regularization is demonstrated in the experiments and the regularized UMLDA algorithm is shown to outperform other multilinear subspace solutions in gait recognition. 1.
Subspace Learning from Image gradient orientations
, 2012
"... We introduce the notion of subspace learning from image gradient orientations for appearance-based object recognition. As image data is typically noisy and noise is substantially different from Gaussian, traditional subspace learning from pixel intensities fails very often to estimate reliably the ..."
Abstract
-
Cited by 17 (9 self)
- Add to MetaCart
We introduce the notion of subspace learning from image gradient orientations for appearance-based object recognition. As image data is typically noisy and noise is substantially different from Gaussian, traditional subspace learning from pixel intensities fails very often to estimate reliably the low-dimensional subspace of a given data population. We show that replacing pixel intensities with gradient orientations and the ℓ2 norm with a cosine-based distance measure offers, to some extend, a remedy to this problem. Within this framework, which we coin IGO (Image Gradient Orientations) subspace learning, we first formulate and study the properties of Principal Component Analysis of image gradient orientations (IGO-PCA). We then show its connection to previously proposed robust PCA techniques both theoretically and experimentally. Finally, we derive a number of other popular subspace learning techniques, namely Linear Discriminant Analysis (LDA), Locally Linear Embedding (LLE) and Laplacian Eigenmaps (LE). Experimental results show that our algorithms outperform significantly popular methods such as Gabor features and Local Binary Patterns and achieve state-of-the-art performance for difficult problems such as illumination- and occlusion-robust face recognition. In addition to this, the proposed IGO-methods require the eigen-decomposition of simple covariance matrices and are as computationally efficient as their corresponding ℓ2 norm intensity-based counterparts. Matlab code for the methods presented in this paper can be found at
Face recognition with disguise and single gallery images
- Image and Vision Computing
, 2007
"... This paper presents a face recognition algorithm that addresses two major challenges. The first is when an individual intentionally alters the appearance and features using disguises, and the second is when limited gallery images are available for recognition. The algorithm uses a dynamic neural net ..."
Abstract
-
Cited by 17 (7 self)
- Add to MetaCart
(Show Context)
This paper presents a face recognition algorithm that addresses two major challenges. The first is when an individual intentionally alters the appearance and features using disguises, and the second is when limited gallery images are available for recognition. The algorithm uses a dynamic neural network architecture to extract the phase features of the face texture using 2D log polar Gabor transform. The phase features are divided into frames which are matched using the Hamming distance. The performance of the proposed algorithm is evaluated using three databases that comprise of real and synthetic face images with different disguise artifacts. The performance of the algorithm is evaluated for decreasing number of gallery images and various types of disguises. In all cases the proposed algorithm shows a better performance compared to other existing algorithms.
On the dimensionality of face space
- IEEE Transactions of Pattern Analysis and Machine Intelligence
"... Abstract—The dimensionality of face space is measured objectively in a psychophysical study. Within this framework, we obtain a measurement of the dimension for the human visual system. Using an eigenface basis, evidence is presented that talented human observers are able to identify familiar faces ..."
Abstract
-
Cited by 14 (1 self)
- Add to MetaCart
(Show Context)
Abstract—The dimensionality of face space is measured objectively in a psychophysical study. Within this framework, we obtain a measurement of the dimension for the human visual system. Using an eigenface basis, evidence is presented that talented human observers are able to identify familiar faces that lie in a space of roughly 100 dimensions and the average observer requires a space of between 100 and 200 dimensions. This is below most current estimates. It is further argued that these estimates give an upper bound for face space dimension and this might be lowered by better constructed “eigenfaces ” and by talented observers. Index Terms—Face and gesture recognition, computational models of vision, psychology, singular value decomposition. 1
Robust Facial Landmarking for Registration
- Annals of Telecommunications
"... Finding landmark positions on facial images is an important step in face registration and normalization, for both 2D and 3D face recognition. In this paper, we inspect shortcomings of existing approaches in the literature and compare several methods for performing automatic landmarking on near-front ..."
Abstract
-
Cited by 13 (7 self)
- Add to MetaCart
(Show Context)
Finding landmark positions on facial images is an important step in face registration and normalization, for both 2D and 3D face recognition. In this paper, we inspect shortcomings of existing approaches in the literature and compare several methods for performing automatic landmarking on near-frontal faces in different scales. Two novel methods have been employed to analyze facial features in coarse and fine scales successively. The first method uses a mixture of factor analyzers to learn Gabor filter outputs on a coarse scale. The second method is a template matching of block-based Discrete Cosine Transform (DCT) features. In addition, a structural analysis subsystem is proposed that can determine false matches, and correct their positions. Key words:
Face recognition using principal component analysis and wavelet packet decomposition
- Informatica
, 2004
"... Abstract. In this article we propose a novel Wavelet Packet Decomposition (WPD)-based modification of the classical Principal Component Analysis (PCA)-based face recognition method. The proposed modification allows to use PCA-based face recognition with a large number of training images and perform ..."
Abstract
-
Cited by 12 (0 self)
- Add to MetaCart
(Show Context)
Abstract. In this article we propose a novel Wavelet Packet Decomposition (WPD)-based modification of the classical Principal Component Analysis (PCA)-based face recognition method. The proposed modification allows to use PCA-based face recognition with a large number of training images and perform training much faster than using the traditional PCA-based method. The proposed method was tested with a database containing photographies of 423 persons and achieved 82–89 % first one recognition rate. These results are close to that achieved by the classical PCAbased method (83–90%). Key words: face recognition, PCA, Wavelet Packet Decomposition, WPD. 1.
Learning discriminant person specific facial models using expandable graphs
- IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
, 2007
"... In this paper, a novel algorithm for finding discriminant person-specific facial models is proposed and tested for frontal face verification. The most discriminant features of a person’s face are found and a deformable model is placed in the spatial coordinates that correspond to these discriminant ..."
Abstract
-
Cited by 11 (7 self)
- Add to MetaCart
(Show Context)
In this paper, a novel algorithm for finding discriminant person-specific facial models is proposed and tested for frontal face verification. The most discriminant features of a person’s face are found and a deformable model is placed in the spatial coordinates that correspond to these discriminant features. The discriminant deformable models, for verifying the person’s identity, that are learned through this procedure are elastic graphs that are dense in the facial areas considered discriminant for a specific person and sparse in other less significant facial areas. The discriminant graphs are enhanced by a discriminant feature selection method for the graph nodes in order to find the most discriminant jet features. The proposed approach significantly enhances the performance of elastic graph matching in frontal face verification.
Combined 2D / 3D Face Recognition using Log-Gabor Templates
, 2006
"... The addition of Three Dimensional (3D) data has the potential to greatly improve the accuracy of Face Recognition Technologies by providing complementary information. In this paper a new method combining intensity and range images and providing insensitivity to expression variation based on Log-Gabo ..."
Abstract
-
Cited by 11 (0 self)
- Add to MetaCart
(Show Context)
The addition of Three Dimensional (3D) data has the potential to greatly improve the accuracy of Face Recognition Technologies by providing complementary information. In this paper a new method combining intensity and range images and providing insensitivity to expression variation based on Log-Gabor Templates is presented. By breaking a single image into 75 semi-independent observations the reliance of the algorithm upon any particular part of the face is relaxed allowing robustness in the presence of occulusions, distortions and facial expressions. Also presented is a new distance measure based on the Mahalanobis Cosine metric which has desirable discriminatory characteristics in both the 2D and 3D domains. Using the 3D database collected by University of Notre Dame for the Face Recognition Grand Challenge (FRGC), benchmarking results are presented demonstrating the performance of the proposed methods.
