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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 knownFace recognition is one of the most important abilities that we use in our daily lives. There are several reasons for the growing interest in automated face recognition , including rising concerns for public security, the need for identity verification for physical and logical access

A direct LDA algorithm for high-dimensional data with application to face recognition
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Linear discriminant analysis (LDA) has been successfully used as a dimensionality reduction technique to many classi cation problems, such as speech recognition , face recognition , and multimedia information retrieval. The objective is to nd a projection A that

Support vector machines applied to face recognition
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Face recognition is a K class problem. where K is the number of known individuals; and support vector machines (SVMs) are a binary classification method. By reformulating the face recognition problem and reinterpreting the output of the SVM classifier. we developed a

Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods.
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Abstract Principal Component Analysis and Fisher Linear Discriminant methods have demonstrated their success in face detection, recognition and tracking. The representations in these subspace methods are based on second order statistics of the image set, and do

A feature based approach to face recognition
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Faces represent one of the most common visual patterns in our environment, and humans have a remarkable ability to recognize faces. Face recognition does not fit into the traditional approaches of model based recognition in vision. We present here a feature based

Face recognition using hidden Markov models
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This dissertation introduces work on face recognition using a novel technique based on Hidden Markov Models (HMMs). Through the integration of a priori structural knowl-edge with statistical information, HMMs can be used successfully to encode face features. The

Face recognition by elastic bunch graph matching
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We present a system for recognizing human faces from single images out of a large database with one image per person. The task is di cult because of image variance in terms of position, size, expression and pose. The system collapses most of this variance byImages of faces, represented as high-dimensional pixel arrays, often belong to a manifold of intrinsically low dimension. Face recognition , and computer vision research in general, has witnessed a growing interest in techniques that capitalize on this observation and applyThe last decade has seen automatic face recognition evolve from small-scale research systems to a wide range of commercial products. Driven by the FERET face database and evaluation protocol, the currently best commercial systems achieve verification accuracies

Openface: A general-purpose face recognition library with mobile applications
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Cameras are becoming ubiquitous in the Internet of Things (IoT) and can use face recognition technology to improve context. There is a large accuracy gap between todays publicly available face recognition systems and the state-of-the-art private face recognition

Improved face recognition rate using HOG features and SVM classifier
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A novel face recognition algorithm is presented in this paper. Histogram of Oriented Gradient features are extracted both for the test image and also for the training images and given to the Support Vector Machine classifier. The detailed steps of HOG feature extraction and the

Face recognition using principle component analysis
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The Principal Component Analysis (PCA) is one of the most successful techniques that have been used in image recognition and compression. PCA is a statistical method under the broad title of factor analysis. The purpose of PCA is to reduce the large dimensionality of the data space

Face -space models of face recognition
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Recent research into face processing has produced considerable technical and theoretical advances. For example, it is possible to generate photographic-quality colour caricatures of faces; principal component analysis can be used to provide efficient storage of facial

Feature selection in face recognition : A sparse representation perspective
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In this paper, we examine the role of feature selection in face recognition from the perspective of sparse representation. We cast the recognition problem as finding a sparse representation of the test image features wrt the training set. The sparse representation can

An introduction to face recognition technology
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Recently face recognition is attracting much attention in the society of network multimedia information access. Areas such as network security, content indexing and retrieval, and video compression benefits from face recognition technology because people are the

Face recognition using particle swarm optimization-based selected features
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Feature selection (FS) is a global optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. It is the most important step that affects the performance of

Independent components of face images: A representation for face recognition
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Methods for obtaining representations of face images based on independent component analysis (ICA) are presented. A global ICA representation is compared to a global representation based on principal component analysis (PCA) for recognizing faces across

3D pose estimation and normalization for face recognition
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Automatic face recognition has been a difficult problem in the field of computer vision for many years. Robust face recognition requires the ability to recognize identity despite many variations in appearance the face can have in a scene. We propose preceding recognition

Evidence for an own-age bias in face recognition .
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Method Participants. Participants were 94 Caucasian individuals from three age groups (18- 25 year olds, 35-45 year olds, and 55-78 year olds). The youngest age group was made up of 42 students from Arizona State University at the West Campus who participated as part of CSE PROJECTS

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