Most face databases have been created under controlled conditions to facilitate the study of specific parameters on the face recognition problem. These parameters include such variables as position, pose, lighting, background, camera quality, and gender. While there are many applications for face recognition technology in which one can control the parameters of image acquisition, there are also many applications in which the practitioner has little or no control over such parameters. This database, Labeled Faces in the Wild, is provided as an aid in studying the latter, unconstrained, recognition problem. The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life. The database exhibits “natural” variability in factors such as pose, lighting, race, accessories, occlusions, and background. In addition to describing the details of the database, we provide specific experimental paradigms for which the database is suitable. This is done in an effort to make research performed with the database as consistent and comparable as possible. We provide baseline results, including results of a state of the art face recognition system combined with a face alignment system. To facilitate experimentation on the database, we provide several parallel databases, including an aligned version.

/pdf/labeled-faces-in-the-wild-a-database-forstudying-face-2prti1m53k.pdf

Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments

Images containing faces are essential to intelligent vision-based human-computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face, regardless of its 3D position, orientation and lighting conditions. Such a problem is challenging because faces are non-rigid and have a high degree of variability in size, shape, color and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.

/pdf/detecting-faces-in-images-a-survey-4yhbbns6mr.pdf

Detecting faces in images: a survey

This paper presents a method for face recognition across variations in pose, ranging from frontal to profile views, and across a wide range of illuminations, including cast shadows and specular reflections. To account for these variations, the algorithm simulates the process of image formation in 3D space, using computer graphics, and it estimates 3D shape and texture of faces from single images. The estimate is achieved by fitting a statistical, morphable model of 3D faces to images. The model is learned from a set of textured 3D scans of heads. We describe the construction of the morphable model, an algorithm to fit the model to images, and a framework for face identification. In this framework, faces are represented by model parameters for 3D shape and texture. We present results obtained with 4,488 images from the publicly available CMU-PIE database and 1,940 images from the FERET database.

/pdf/face-recognition-based-on-fitting-a-3d-morphable-model-4yq0vqb68g.pdf

Face recognition based on fitting a 3D morphable model

In this paper, we describe the acquisition and contents of a large-scale Chinese face database: the CAS-PEAL face database. The goals of creating the CAS-PEAL face database include the following: 1) providing the worldwide researchers of face recognition with different sources of variations, particularly pose, expression, accessories, and lighting (PEAL), and exhaustive ground-truth information in one uniform database; 2) advancing the state-of-the-art face recognition technologies aiming at practical applications by using off-the-shelf imaging equipment and by designing normal face variations in the database; and 3) providing a large-scale face database of Mongolian. Currently, the CAS-PEAL face database contains 99 594 images of 1040 individuals (595 males and 445 females). A total of nine cameras are mounted horizontally on an arc arm to simultaneously capture images across different poses. Each subject is asked to look straight ahead, up, and down to obtain 27 images in three shots. Five facial expressions, six accessories, and 15 lighting changes are also included in the database. A selected subset of the database (CAS-PEAL-R1, containing 30 863 images of the 1040 subjects) is available to other researchers now. We discuss the evaluation protocol based on the CAS-PEAL-R1 database and present the performance of four algorithms as a baseline to do the following: 1) elementarily assess the difficulty of the database for face recognition algorithms; 2) preference evaluation results for researchers using the database; and 3) identify the strengths and weaknesses of the commonly used algorithms.

http://www.jdl.ac.cn/doc/2008/IEEE_SMC_A_gao_CAS-PEAL_Fianl.pdf

The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations

Low-dimensional feature representation with enhanced discriminatory power is of paramount importance to face recognition (FR) systems. Most of traditional linear discriminant analysis (LDA)-based methods suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the "small sample size" (SSS) problem which is often encountered in FR tasks. In this paper, we propose a new algorithm that deals with both of the shortcomings in an efficient and cost effective manner. The proposed method is compared, in terms of classification accuracy, to other commonly used FR methods on two face databases. Results indicate that the performance of the proposed method is overall superior to those of traditional FR approaches, such as the eigenfaces, fisherfaces, and D-LDA methods.

/pdf/face-recognition-using-lda-based-algorithms-3vei9pjcy7.pdf

Face recognition using LDA-based algorithms

We describe an eigenspace manifold for the representation and recognition of pose-varying faces. The distribution of faces in this manifold allows us to determine theoretical recognition characteristics which are then verified experimentally. Using this manifold a framework is proposed which can be used for both familiar and unfamiliar face recognition. A simple implementation demonstrates the pose dependent nature of the system over the transition from unfamiliar to familiar face recognition. Furthermore we show that multiple test images, whether real or virtual, can be used to augment the recognition process. The results compare favourably with reported human face recognition experiments. Finally, we describe how this framework can be used as a mechanism for characterising faces from video for general purpose recognition.

Characterising Virtual Eigensignatures for General Purpose Face Recognition

A framework for recognising human faces from unfamiliar views is described and a simple implementation of this framework evaluated. The interaction between training view and testing view is shown to compare with observations in human face recognition experiments. The ability of the system to learn from several training views, as available in video footage, is shown to improve the overall performance of the system as is the use of multiple testing images.

Face recognition from unfamiliar views: subspace methods and pose dependency

This paper proposes a novel system for face recognition which utilises the parametric eigenspace of Murase and Nayar (1993) to determine both the pose and identification of a face held within a current face database. In order that all views from profile to frontal are available for recognition a sample of views are taken from a range of angles so that, during the identification process, virtual views can be estimated from a novel view to supply ordered coordinates in the eigenvector subspace for the recognition algorithm. The method is demonstrated on a set of faces and results show that the technique is practical. The system is described in the context of a theoretical model of human face recognition which closely resembles that described in the literature

Face recognition using virtual parametric eigenspace signatures

Increasingly the problems of recognising faces under a variety of viewing conditions, including depth rotations, is being considered in the field. The concept of norm-based coding in face recognition is not new but has been little investigated in machine models. Here we describe a norm-based face recognition system which is capable of generalising from a single training view to recognise novel views of target faces. The system is based upon the characteristic nature faces as they move through a pose-varying eigenspace of facial images and deviations from the norm of a gallery of face images. We illustrate the use of the technique for a large range of pose variation.

Norm/sup 2/-based face recognition

A working face recognition system requires the ability to represent facial images in such a way that permits efficient and accurate processing. The human visual system effectively stores, recognises and classifies familiar facial images under a wide variety of viewing conditions, albeit with various degrees of accuracy. We describe a system which automatically determines a representation for pose-varying facial images - a representation with inherent classification , an ability to generalise from one viewing condition to another, and which uses fast computational procedures.

/pdf/automatic-face-representation-and-classification-3fsixujhdm.pdf

Automatic face representation and classification

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