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Three-dimensional face recognition

About: Three-dimensional face recognition is a research topic. Over the lifetime, 7270 publications have been published within this topic receiving 256709 citations.


Papers
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Journal ArticleDOI
TL;DR: A comprehensive and critical survey of face detection algorithms, ranging from simple edge-based algorithms to composite high-level approaches utilizing advanced pattern recognition methods, is presented.

1,565 citations

Proceedings ArticleDOI
02 Sep 2009
TL;DR: This paper publishes a generative 3D shape and texture model, the Basel Face Model (BFM), and demonstrates its application to several face recognition task and publishes a set of detailed recognition and reconstruction results on standard databases to allow complete algorithm comparisons.
Abstract: Generative 3D face models are a powerful tool in computer vision. They provide pose and illumination invariance by modeling the space of 3D faces and the imaging process. The power of these models comes at the cost of an expensive and tedious construction process, which has led the community to focus on more easily constructed but less powerful models. With this paper we publish a generative 3D shape and texture model, the Basel Face Model (BFM), and demonstrate its application to several face recognition task. We improve on previous models by offering higher shape and texture accuracy due to a better scanning device and less correspondence artifacts due to an improved registration algorithm. The same 3D face model can be fit to 2D or 3D images acquired under different situations and with different sensors using an analysis by synthesis method. The resulting model parameters separate pose, lighting, imaging and identity parameters, which facilitates invariant face recognition across sensors and data sets by comparing only the identity parameters. We hope that the availability of this registered face model will spur research in generative models. Together with the model we publish a set of detailed recognition and reconstruction results on standard databases to allow complete algorithm comparisons.

1,265 citations

Proceedings Article
01 Sep 2008
TL;DR: The CMU Multi-PIE database as mentioned in this paper contains 337 subjects, imaged under 15 view points and 19 illumination conditions in up to four recording sessions, with a limited number of subjects, a single recording session and only few expressions captured.
Abstract: A close relationship exists between the advancement of face recognition algorithms and the availability of face databases varying factors that affect facial appearance in a controlled manner. The CMU PIE database has been very influential in advancing research in face recognition across pose and illumination. Despite its success the PIE database has several shortcomings: a limited number of subjects, a single recording session and only few expressions captured. To address these issues we collected the CMU Multi-PIE database. It contains 337 subjects, imaged under 15 view points and 19 illumination conditions in up to four recording sessions. In this paper we introduce the database and describe the recording procedure. We furthermore present results from baseline experiments using PCA and LDA classifiers to highlight similarities and differences between PIE and Multi-PIE.

1,181 citations

Proceedings ArticleDOI
07 Mar 2016
TL;DR: OpenFace is the first open source tool capable of facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation and allows for easy integration with other applications and devices through a lightweight messaging system.
Abstract: Over the past few years, there has been an increased interest in automatic facial behavior analysis and understanding. We present OpenFace — an open source tool intended for computer vision and machine learning researchers, affective computing community and people interested in building interactive applications based on facial behavior analysis. OpenFace is the first open source tool capable of facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation. The computer vision algorithms which represent the core of OpenFace demonstrate state-of-the-art results in all of the above mentioned tasks. Furthermore, our tool is capable of real-time performance and is able to run from a simple webcam without any specialist hardware. Finally, OpenFace allows for easy integration with other applications and devices through a lightweight messaging system.

1,151 citations

Proceedings ArticleDOI
26 Dec 2007
TL;DR: This paper describes face data as resulting from a generative model which incorporates both within- individual and between-individual variation, and calculates the likelihood that the differences between face images are entirely due to within-individual variability.
Abstract: Many current face recognition algorithms perform badly when the lighting or pose of the probe and gallery images differ. In this paper we present a novel algorithm designed for these conditions. We describe face data as resulting from a generative model which incorporates both within-individual and between-individual variation. In recognition we calculate the likelihood that the differences between face images are entirely due to within-individual variability. We extend this to the non-linear case where an arbitrary face manifold can be described and noise is position-dependent. We also develop a "tied" version of the algorithm that allows explicit comparison across quite different viewing conditions. We demonstrate that our model produces state of the art results for (i) frontal face recognition (ii) face recognition under varying pose.

1,099 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202374
2022208
20215
202014
201918
201842