Facial Face Recognition Method using Fourier Transform Filters Gabor and R_LDA (original) (raw)
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Face Recognition using Gabor Filters
Journal of Applied Computer …, 2011
Gabor-based face representation has achieved enormous success in face recognition. This paper addresses a novel algorithm for face recognition using neural networks trained by Gabor features. The system is commenced on convolving a face image with a series of Gabor filter coefficients at different scales and orientations. Two novel contributions of this paper are: scaling of rms contrast and introduction of fuzzily skewed filter. The neural network employed for face recognition is based on the multilayer perceptron (MLP) architecture with backpropagation algorithm and incorporates the convolution filter response of Gabor jet. The effectiveness of the algorithm has been justified over a face database with images captured at different illumination conditions.
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IOSR Journal of Computer Engineering, 2012
Face recognition is a hot research topic in the fields of pattern recognition and computer vision, which has been found a widely used in many applications, such as verification of credit card, security access control, and human computer interface. As a result, numerous face recognition algorithms have been proposed, and surveys in this area can be found. Although many approaches for face recognition have been proposed in the past, none of them can overcome the main problem of lighting, pose and orientation. For a real time face recognition system, these constraints are to be a major Analysis (PCA) .These methods challenge which has to be addressed. In this proposed system, a methodology is given for improving the robustness of a face recognition system based on two well-known statistical modelling methods to represent a face image: Principal Component extract the discriminates features from the face. Preprocessing of human face image is done using Gabor wavelets which eliminates the variations due to pose, lighting and features to some extent. PCA extract low dimensional and discriminating feature vectors and these feature vectors were used for classification. The classification stage uses nearest neighbour as classifier. This proposed system will use the YALE face data base with 100 frontal images corresponding to10 different subjects of variable illumination and facial expressions.
A Novel Gabor-LDA Based Face Recognition Method
Lecture Notes in Computer Science, 2004
In this paper, a novel face recognition method based on Gabor-wavelet and linear discriminant analysis (LDA) is proposed. Given training face images, discriminant vectors are computed using LDA. The function of the discriminant vectors is twofold. First, discriminant vectors are used as a transform matrix, and LDA features are extracted by projecting original intensity images onto discriminant vectors. Second, discriminant vectors are used to select discriminant pixels, the number of which is much less than that of a whole image. Gabor features are extracted only on these discriminant pixels. Then, applying LDA on the Gabor features, one can obtain the Gabor-LDA features. Finally, a combined classifier is formed based on these two types of LDA features. Experimental results show that the proposed method performs better than traditional approaches in terms of both efficiency and accuracy... .
This paper presents a novel face recognition method based on the Gabor filter bank, Kernel Principle Component Analysis (KPCA) and Support Vector Machine (SVM). At first, the Gabor filter bank with 5 frequencies and 8 orientations is applied on each face image to extract robust features against local distortions caused by variance of illumination, facial expression and pose. Then, the feature reduction technique of KPCA is performed on the outputs of the filter bank to form the new low-dimensional feature vectors. Finally, SVM is used for classification of the extracted features. The proposed method is tested on the ORL face database. The experimental results reveal that the proposed method has a maximum recognition rate of 98.5% which is higher than the other related algorithms applied on the ORL database.
Face Recognition Algorithm Using the Discrete Gabor Transform
17th International Conference on Electronics, Communications and Computers (CONIELECOMP'07), 2007
This paper proposes a Face Recognition Algorithm in which the Discrete Gabor transform is used to extract the image face features vector that is then feed into a multilayer perceptron to carried out the recognition task. The features vector, estimated using the Gabor Transform, presents a small intra-person variation while the inter-persons variation is considerably large. This fact provides robustness against changes in illumination, wardrobe, facial expressions, scale, and position inside the captured image, as well as inclination, noise contamination and filtering. Proposed scheme also provides some tolerance to changes on the age of the person under analysis. Evaluation results using the proposed scheme with identification and verification configurations are given to show the desirable features of proposed algorithm
Gabor-based kernel-partial-least-squares discrimination features for face recognition
The paper presents a novel method for the extraction of facial features based on the Gabor-wavelet representation of face images and the kernel partial-least-squares discrimination (KPLSD) algorithm. The proposed feature-extraction method, called the Gabor-based kernel partial-least-squares discrimination (GKPLSD), is performed in two consecutive steps. In the first step a set of forty Gabor wavelets is used to extract discriminative and robust facial features, while in the second step the kernel partial-least-squares discrimination technique is used to reduce the dimensionality of the Gabor feature vector and to further enhance its discriminatory power. For optimal performance, the KPLSD-based transformation is implemented using the recently proposed fractional-power-polynomial models. The experimental results based on the XM2VTS and ORL databases show that the GKPLSD approach outperforms feature-extraction methods such as principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA) or generalized discriminant analysis (GDA) as well as combinations of these methods with Gabor representations of the face images. Furthermore, as the KPLSD algorithm is derived from the kernel partial-least squares regression (KPLSR) model it does not suffer from the small-sample-size problem, which is regularly encountered in the field of face recognition. Keywords: face recognition, Gabor filters, Gabor wavelets, kernel methods, subspace projection, partial least squares, principal component analysis, linear discriminant analysis, XM2VTS database, ORL database
On face recognition using gabor filters
Proceedings of world academy of science, …, 2007
Gabor-based face representation has achieved enormous success in face recognition. This paper addresses a novel algorithm for face recognition using neural networks trained by Gabor features. The system is commenced on convolving a face image with a series of Gabor filter coefficients at different scales and orientations. Two novel contributions of this paper are: scaling of rms contrast and introduction of fuzzily skewed filter. The neural network employed for face recognition is based on the multilayer perceptron (MLP) architecture with backpropagation algorithm and incorporates the convolution filter response of Gabor jet. The effectiveness of the algorithm has been justified over a face database with images captured at different illumination conditions.
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Lecture Notes in Computer Science, 2007
A novel Support Vector Machine (SVM) face recognition method using optimized Gabor features is presented in this paper. 200 Gabor features are first selected by a boosting algorithm, which are then combined with SVM to build a two-class based face recognition system. While computation and memory cost of the Gabor feature extraction process has been significantly reduced, our method has achieved the same accuracy as a Gabor feature and Linear Discriminant Analysis (LDA) based multi-class system.