The Photoface database

Zafeiriou, Stefanos; Hansen, Mark F.; Atkinson, Gary A.; Argyriou, Vasileios; Petrou, Maria; Smith, Melvyn L.; Smith, Lyndon

doi:10.1109/cvprw.2011.5981840

Cited by 43 publications

(64 citation statements)

References 23 publications

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“…The first set is quantitative experiments on the Photoface database [69] where we consider as ground-truth the normals produced by the calibrated 4-source Photometric Stereo. For the purposes of this experiment we have withheld 100 subjects from the training set of all algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…The databases that contain samples captured under different illuminations include YALE-B [16], PIE [53] and MULTI-PIE [20], as well as the recently collected Photoface database [69]. The Photoface database [69] was collected using a custom-made four-source PS device designed to enable data capture with minimal interaction with people.…”

Section: Databases Of Facial Normalsmentioning

confidence: 99%

“…The Photoface database [69] was collected using a custom-made four-source PS device designed to enable data capture with minimal interaction with people. The device was placed at the entrance to a busy workplace and captured many sessions from more than 450 people displaying various expressions.…”

Section: Databases Of Facial Normalsmentioning

confidence: 99%

“…The last database we used was the Photoface database [69]. In the Photoface database, each session contains four images captured under a different illumination.…”

Section: Data From the Photoface Databasementioning

confidence: 99%

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Face Normals "In-the-Wild" Using Fully Convolutional Networks

Trigeorgis

Snape

Kokkinos

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Self Cite

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In this work we pursue a data-driven approach to the problem of estimating surface normals from a single intensity image, focusing in particular on human faces. We introduce new methods to exploit the currently available facial databases for dataset construction and tailor a deep convolutional neural network to the task of estimating facial surface normals 'in-the-wild'. We train a fully convolutional network that can accurately recover facial normals from images including a challenging variety of expressions and facial poses. We compare against state-of-the-art face Shape-from-Shading and 3D reconstruction techniques and show that the proposed network can recover substantially more accurate and realistic normals. Furthermore, in contrast to other existing face-specific surface recovery methods, we do not require the solving of an explicit alignment step due to the fully convolutional nature of our network.

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Section: Methodsmentioning

confidence: 99%

Section: Databases Of Facial Normalsmentioning

confidence: 99%

Section: Databases Of Facial Normalsmentioning

confidence: 99%

“…The last database we used was the Photoface database [69]. In the Photoface database, each session contains four images captured under a different illumination.…”

Section: Data From the Photoface Databasementioning

confidence: 99%

See 2 more Smart Citations

Face Normals "In-the-Wild" Using Fully Convolutional Networks

Trigeorgis

Snape

Kokkinos

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Self Cite

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show abstract

“…The results were automatically cropped to a central portion of the face and resized to an 80×80 pixel square and added to the Photoface database. 31,32 The new, augmented database was then processed to consist of only the x-gradients, ensuring compatibility with two source data. Our long range test images were likewise cropped and resized.…”

Section: Face Recognitionmentioning

confidence: 99%

Long-range concealed object detection through active covert illumination

et al. 2015

Self Cite

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When capturing a scene for surveillance, the addition of rich 3D data can dramatically improve the accuracy of object detection or face recognition. Traditional 3D techniques, such as geometric stereo, only provide a coarse grained reconstruction of the scene and are ill-suited to fine analysis. Photometric stereo is a well established technique providing dense, high-resolution, reconstructions, using active artificial illumination of an object from multiple directions to gather surface information. It is typically used indoors, at short range (<1 m), under highly controlled lighting conditions, and so has rarely found application in real-world, outdoor surveillance. One difficulty in such scenes is that the near infrared light generally used to illuminate a scene is overwhelmed by ambient illumination from the sun, and so it is not possible to reliably discriminate the artificial light from the ambient. This work aims to overcome this limitation and counteract the effect of sunlight by exploiting atmospheric absorption at a particular wavelength, 940 nm, in combination with image capture using state-of-the-art black silicon sensors. We first show that this combination, together with low-cost, focused LED illuminators, allows us to reliably differentiate artificial illumination from ambient lighting. We then extend our system to perform photometric stereo reconstruction at distances far beyond the current state of the art under direct sunlight, with a possibility for extending the range to 100 m and beyond. Finally, we provide some example use cases for the photometric stereo data in the form of detection of concealed objects and face recognition.

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3D Face Analysis: Advances and Perspectives

Huang

Sun

Yang

et al. 2014

Biometric Recognition

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Abstract. In the past decade, research on 3D face analysis has been extensively developed, and this study briefly reviews the progress achieved in data acquisition, algorithms, and experimental methodologies, for the issues of face recognition, facial expression recognition, gender and ethnicity classification, age estimation, etc., especially focusing on that after the availability of FRGC v2.0. It further points out several challenges to deal with for more efficient and reliable systems in the real world.

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The Photoface database

Cited by 43 publications

References 23 publications

Face Normals "In-the-Wild" Using Fully Convolutional Networks

Face Normals "In-the-Wild" Using Fully Convolutional Networks

Long-range concealed object detection through active covert illumination

3D Face Analysis: Advances and Perspectives

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