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Neural Networks have been widely used in face recognition as a reliable classifier. In the proposed method, neural network classifier with CSD coefficients is used to speed up the recognition system. The FPGA implementation of the proposed method indicates that the high speed recognition can be achieved by using neural network classifier with CSD coefficients while maintaining good recognition rate.
A new method for face recognition based on weighted votes on different sub-images of a picture is proposed. The proposed method is robust under illumination variations and achieves the illumination invariants based on the reflectance-illumination model. The proposed method does not require any prior information about the face shape or illumination and can be applied on each image separately. It does...
An efficient method for face recognition which is robust under illumination variations is proposed. The proposed method achieves the illumination invariants based on the reflectance-illumination model. Different high-pass filters have been tested to achieve the reflectance part of the image which is illumination invariant and maximum filter is proposed as the best method for this purpose. The proposed...
In this paper, we propose a wavelet based feature extraction method with a high tolerance to white Gaussian noise. This method is also computationally efficient. Along with an HMM classifier, this method is used for face recognition. High recognition rates in the presence of white Gaussian noises with different variances show this technique as a promising feature extraction method.
Face recognition, the main biometric used by human beings, has plenty of applications in identity validation and recognition and has become one of the hottest topics in the area of image processing and pattern recognition. Pseudo-Zernike moment invariant was chosen as the feature extractor due to its good results among other moment invariants. In this paper pseudo-Zernike moment invariant has been...
Human face recognition has recently become one of the hottest topics in the area of pattern recognition due to its applications in identity validation and recognition. Moment Invariants are pattern sensitive features and are used in pattern recognition applications. In this paper different moment invariants have been used to extract features from human face images for recognition application. Moment...
Traditional subspace methods for face recognition, from the original eigenfaces technique to the recently introduced Laplacian faces method, measure the similarity between images after projecting them onto a face subspace. In this paper, we present a robust face recognition system that uses a neural classifier and Laplacian faces method. Computer simulation shows that the proposed algorithm has better...
Pseudo-Zernike polynomials are well known and widely used in the analysis of optical systems. In this paper, we introduce a weighted pseudo-Zernike feature for face recognition. The EA strategy is used to maximize the Fisher linear discriminant function (FLD) over the Pseudo-Zernike moments. The argument, which maximizes the FLD criteria, is selected as the proposed weight function. To evaluate the...
This paper introduces a new method for the recognition of human faces in 2-dimensional digital images using a new localization of facial information and Pseudo Zernike Moment Invariants (PZMI) as features and a radial basis function (RBF) neural network as the classifier. In this paper the effect of two parameters in recognition rate improvement are studied. These include the order of the PZMI as...
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