Results 1 - 10
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438
Active Appearance Models.
- IEEE Transactions on Pattern Analysis and Machine Intelligence,
, 2001
"... AbstractÐWe describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations ..."
Abstract
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Cited by 2154 (59 self)
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AbstractÐWe describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations in the model parameters and the induced image errors.
Face Recognition: A Literature Survey
, 2000
"... ... This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into ..."
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Cited by 1398 (21 self)
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... This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition,
Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments
"... Face recognition has benefitted greatly from the many databases that have been produced to study it. Most of these 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 posi ..."
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Cited by 449 (11 self)
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Face recognition has benefitted greatly from the many databases that have been produced to study it. Most of these 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, expression, background, camera quality, occlusion, age, 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 is provided as an aid in studying the latter, unconstrained, face recognition problem. The database represents an initial attempt to provide a set of labeled face photographs spanning the range of conditions typically encountered by people in their everyday lives. The database exhibits “natural ” variability in pose, lighting, focus, resolution, facial expression, age, gender, race, accessories, make-up, occlusions, background, and photographic quality. Despite this variability, the images in the database are presented in a simple and consistent format for maximum ease of use. In addition to describing the details of the database and its acquisition, 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.
Parts Of Recognition
- COGNITION
, 1983
"... A complete theory of object recognition is an impossibility not simply because of the multiplicity of visual cues we exploit in elegant coordination to identify an object, but primarily because recognition involves fixation of belief, and anything one knows may be relevant. We finesse this obstacle ..."
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Cited by 319 (8 self)
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A complete theory of object recognition is an impossibility not simply because of the multiplicity of visual cues we exploit in elegant coordination to identify an object, but primarily because recognition involves fixation of belief, and anything one knows may be relevant. We finesse this obstacle with two moves. The first restricts attention to one visual cue, the shapes of objects; the second restricts attention to one problem, the initial guess at the identity of an object. We propose that the visual system decomposes a shape into parts, that it does so using a rule defining part boundaries rather than part shapes, that the rule exploits a uniformity of nature -- transversality, and that parts with. their descriptions and spatial relations provide a first index into a memory of shapes. These rules lead to a more comprehensive explanation of several visual illusions. The role of inductive inference is stressed in our theory. We conclude with a precis of unsolved problems.
Robust Face Detection Using the Hausdorff Distance
, 2001
"... The localization of human faces in digital images is a fundamental step in the process of face recognition. This paper presents a shape comparison approach to achieve fast, accurate face detection that is robust to changes in illumination and background. The proposed method is edge-based and works o ..."
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Cited by 212 (1 self)
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The localization of human faces in digital images is a fundamental step in the process of face recognition. This paper presents a shape comparison approach to achieve fast, accurate face detection that is robust to changes in illumination and background. The proposed method is edge-based and works on grayscale still images. The Hausdorff distance is used as a similarity measure between a general face model and possible instances of the object within the image. The paper describes an efficient implementation, making this approach suitable for real-time applications. A two-step process that allows both coarse detection and exact localization of faces is presented. Experiments were performed on a large test set base and rated with a new validation measurement.
Recent advances in the automatic recognition of audiovisual speech
- Proceedings of the IEEE
"... Abstract — Visual speech information from the speaker’s mouth region has been successfully shown to improve noise robustness of automatic speech recognizers, thus promising to extend their usability into the human computer interface. In this paper, we review the main components of audio-visual autom ..."
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Cited by 172 (16 self)
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Abstract — Visual speech information from the speaker’s mouth region has been successfully shown to improve noise robustness of automatic speech recognizers, thus promising to extend their usability into the human computer interface. In this paper, we review the main components of audio-visual automatic speech recognition and present novel contributions in two main areas: First, the visual front end design, based on a cascade of linear image transforms of an appropriate video region-of-interest, and subsequently, audio-visual speech integration. On the later topic, we discuss new work on feature and decision fusion combination, the modeling of audio-visual speech asynchrony, and incorporating modality reliability estimates to the bimodal recognition process. We also briefly touch upon the issue of audio-visual speaker adaptation. We apply our algorithms to three multi-subject bimodal databases, ranging from small- to large-vocabulary recognition tasks, recorded at both visually controlled and challenging environments. Our experiments demonstrate that the visual modality improves automatic speech recognition over all conditions and data considered, however less so for visually challenging environments and large vocabulary tasks. Index Terms — Audio-visual speech recognition, speechreading, visual feature extraction, audio-visual fusion, hidden Markov model, multi-stream HMM, product HMM, reliability estimation, adaptation, audio-visual databases. I.
A survey of approaches and challenges in 3d and multi-modal 3d+2d face recognition,
- Comp. Vis. and Imag. Understand.
, 2006
"... Abstract This survey focuses on recognition performed by matching models of the three-dimensional shape of the face, either alone or in combination with matching corresponding two-dimensional intensity images. Research trends to date are summarized, and challenges confronting the development of mor ..."
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Cited by 141 (8 self)
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Abstract This survey focuses on recognition performed by matching models of the three-dimensional shape of the face, either alone or in combination with matching corresponding two-dimensional intensity images. Research trends to date are summarized, and challenges confronting the development of more accurate three-dimensional face recognition are identified. These challenges include the need for better sensors, improved recognition algorithms, and more rigorous experimental methodology.
Feature detection and tracking with constrained local models
, 2006
"... We present an efficient and robust model matching method which uses a joint shape and texture appearance model to generate a set of region template detectors. The model is fitted to an unseen image in an iterative manner by generating templates using the joint model and the current parameter estimat ..."
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Cited by 121 (3 self)
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We present an efficient and robust model matching method which uses a joint shape and texture appearance model to generate a set of region template detectors. The model is fitted to an unseen image in an iterative manner by generating templates using the joint model and the current parameter estimates, correlating the templates with the target image to generate response images and optimising the shape parameters so as to maximise the sum of responses. The appearance model is similar to that used in the AAM [1]. However in our approach the appearance model is used to generate likely feature templates, instead of trying to approximate the image pixels directly. We show that when applied to human faces, our Constrained Local Model (CLM) algorithm is more robust and more accurate than the original AAM search method, which relies on the image reconstruction error to update the model parameters. We demonstrate improved localisation accuracy on two publicly available face data sets and improved tracking on a challenging set of in-car face sequences. 1
Audio-visual automatic speech recognition: An overview.
- In Issues in Visual and Audio-Visual Speech Processing.
, 2004
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