face recognition ieee papers


Face recognition using eigenfaces
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We present an approach to the detection and identification of human faces and describe a working, near-real-time face recognition system which tracks a subjects head and then recognizes the person by comparing characteristics of the face to those of known

From few to many: Illumination cone models for face recognition under variable lighting and pose
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ABSTRACT We present a generative appearance-based method for recognizing human faces under variation in lighting and viewpoint. Our method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination

Face recognition : A convolutional neural-network approach
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Faces represent complex multidimensional mean-ingful visual stimuli and developing a computational model for face recognition is difficult. We present a hybrid neural-network solution which compares favorably with other methods. The system combines local image

Face recognition : The problem of compensating for changes in illumination direction
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ABSTRACT b Abstract /b A face recognition system must recognize a face from a novel image despite the variations between images of the same face . A common approach to overcoming image variations because of changes in the illumination conditions is to use

Face recognition using the nearest feature line method
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In this paper, we propose a novel classification method, called the nearest feature line (NFL), for face recognition . Any two feature points of the same class (person) are generalized by the feature line (FL) passing through the two points. The derived FL can

Face recognition using kernel direct discriminant analysis algorithms
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Techniques that can introduce low-dimensional feature representation with enhanced discriminatory power is of paramount importance in face recognition (FR) systems. It is well known that the distribution of face images, under a perceivable variation in viewpoint

Face recognition by humans: Nineteen results all computer vision researchers should know about
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A key goal of computer vision researchers is to create automated face recognition systems that can equal, and eventually surpass, human performance. To this end, it is imperative that computational researchers know of the key findings from experimental studies of face

Face recognition with radial basis function (RBF) neural networks
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A general and efficient design approach using a radial basis function (RBF) neural classifier to cope with small training sets of high dimension, which is a problem frequently encountered in face recognition , is presented in this paper. In order to avoid overfitting and

Gabor-based kernel PCA with fractional power polynomial models for face recognition
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ABSTRACT b Abstract /b 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

Discriminant waveletfaces and nearest feature classifiers for face recognition
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Abstract it Feature extraction , it discriminant analysis, and it classification rule are three crucial issues for face recognition . This paper presents hybrid approaches to handle three issues together. For feature

Beyond eigenfaces: Probabilistic matching for face recognition
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We propose a technique for direct visual matching for face recognition and database search, using a probabilistic measure of similarity which is based on a Bayesian analysis of image differences. Specifically we model two mutually exclusive classes of variation between facial

Learning a spatially smooth subspace for face recognition
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Subspace learning based face recognition methods have attracted considerable interests in recently years, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Locality Preserving Projection (LPP), Neighborhood Preserving Embedding (NPE)

Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition
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This paper introduces a novel Gabor-Fisher 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

High-speed face recognition based on discrete cosine transform and RBF neural networks
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In this paper, an efficient method for high-speed face recognition based on the discrete cosine transform (DCT), the Fishers linear discriminant (FLD) and radial basis function (RBF) neural networks is presented. First, the dimensionality of the original face image is

Effective representation using ICA for face recognition robust to local distortion and partial occlusion
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The performance of face recognition methods using subspace projection is directly related to the characteristics of their basis images, especially in the cases of local distortion or partial occlusion. In order for a subspace projection method to be robust to local distortion and ACE and gesture recognition are effortless aspects of interaction among humans, but human- computer interaction remains based upon signals and behaviors which are not natural for us. Although keyboard and mouse are undeniable improvements over tabular switch and In this paper we investigate the performance of a technique for face recognition based on the computation of 25 local autocorrelation coefficients. We use a large database of 11,600 frontal facial images of 116 persons

A survey of approaches to three-dimensional face recognition
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The vast majority of face recognition research has focused on the use of two-dimensional intensity images, and is covered in existing survey papers. This survey focuses on face recognition using three-dimensional data, either alone or in combination with two

GA-fisher: a new LDA-based face recognition algorithm with selection of principal components
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This paper addresses the dimension reduction problem in Fisherface for face recognition . When the number of training samples is less than the image dimension (total number of pixels), the within-class scatter matrix (Sw) in Linear Discriminant Analysis (LDA) is singular In this paper, we propose a novel line feature-based face recognition algorithm. A face is represented by the Face -ARG model, where all the geometric quantities and the structural information are encoded in an Attributed Relational Graph (ARG) structure, then the partial



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  • IEEE PAPERS ON CSE-COMPUTER SCIENCE ENGINEERING iot-internet-of-things robotics hadoop-technology machine-learning cloud-computing big-data image-processing information-retrieval-ieee-paper information-retrieval-2019 face-recognition-2019 face-recognition-ieee-papers machine-learning-2019-2
  • CSE-IEEE-PAPERS-2019 iot-internet-of-things robotics hadoop-technology machine-learning cloud-computing big-data image-processing information-retrieval-ieee-paper information-retrieval-2019 face-recognition-2019 face-recognition-ieee-papers
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