A 3D Dynamic Database for Unconstrained Face Recognition

Alashkar, Taleb; Amor, Boulbaba Ben; Daoudi, Mohamed

doi:10.15221/14.357

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…[13,47,65]) or dynamic facial expressions (e.g. [2,15,18,44,64,68,69]). Most of these datasets focus on emotional expressions and only a few datasets capture facial dynamics caused by speech.…”

Section: Related Workmentioning

confidence: 99%

Capture, Learning, and Synthesis of 3D Speaking Styles

Cudeiro

Bolkart

Laidlaw

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

266

205

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Input: speech signal and 3D templateOutput: 3D character animation Figure 1: Given an arbitrary speech signal and a static 3D face mesh as input (left), our model, VOCA outputs a realistic 3D character animation (right). Top: Winston Churchill. Bottom: Actor from Karras et al. [33]. See supplementary video. AbstractAudio-driven 3D facial animation has been widely explored, but achieving realistic, human-like performance is still unsolved. This is due to the lack of available 3D datasets, models, and standard evaluation metrics. To address this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio from 12 speakers. We then train a neural network on our dataset that factors identity from facial motion. The learned model, VOCA (Voice Operated Character Animation) takes any speech signal as input-even speech in languages other than English-and realistically animates a wide range of adult faces. Conditioning on subject labels during training allows the model to learn a variety of realistic speaking styles. VOCA also provides animator controls to alter speaking style, identity-dependent facial shape, and pose (i.e. head, jaw, and eyeball rotations) during animation. To our knowledge, VOCA is the only realistic 3D facial animation model that is read-ily applicable to unseen subjects without retargeting. This makes VOCA suitable for tasks like in-game video, virtual reality avatars, or any scenario in which the speaker, speech, or language is not known in advance. We make the dataset and model available for research purposes at

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Section: Related Workmentioning

confidence: 99%

Capture, Learning, and Synthesis of 3D Speaking Styles

Cudeiro

Bolkart

Laidlaw

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

266

205

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“…The acquisitions are then naturally less vulnerable to the frequent face alignment and lighting problems found with RGB-only methods [116]. They could be acquired using high resolution 3D scanners as for the Biwi-3D and the 3D-dynamic-DB datasets [188,189] or with multiple cameras and consumer depth sensors as within the Biwi-kinect [190] and the 4DFAB [191] datasets. A classification into static 3D or dynamic 4D facial datasets is offered in Tab.1.6.…”

Section: Multi-modal Datasets For 3d Fes Recognitionmentioning

confidence: 99%

Visual Methods for Sign Language Recognition: A Modality-Based Review

Seddik¹,

Amara²

2020

Preprint

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Sign language visual recognition from continuous multi-modal streams is still one of the most challenging fields. Recent advances in human actions recognition are exploiting the ascension of GPU-based learning from massive data, and are getting closer to human-like performances. They are then prone to creating interactive services for the deaf and hearing-impaired communities. A population that is expected to grow considerably in the years to come. This paper aims at reviewing the human actions recognition literature with the sign-language visual understanding as a scope. The methods analyzed will be mainly organized according to the different types of unimodal inputs exploited, their relative multi-modal combinations and pipeline steps. In each section, we will detail and compare the related datasets, approaches then distinguish the still open contribution paths suitable for the creation of sign language related services. Special attention will be paid to the approaches and commercial solutions handling facial expressions and continuous signing.

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“…Alashkar et al [3,4] 58 people (avg. 23) N/A Neutral and posed expressions with random poses, occlusion, talking, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Arguably, the main reason is the lack of publicly available high quality 4D databases with many recording sessions that can be used for face recognition/verification. Furthermore, as the commonly used databases [3,45] contain only one recording session per subject, the generalization ability of the tested method is doubtful.…”

mentioning

confidence: 99%

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4DFAB: A Large Scale 4D Database for Facial Expression Analysis and Biometric Applications

Cheng

Kotsia²,

Pantić

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

105

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The progress we are currently witnessing in many computer vision applications, including automatic face analysis, would not be made possible without tremendous efforts in collecting and annotating large scale visual databases. To this end, we propose 4DFAB, a new large scale database of dynamic high-resolution 3D faces (over 1,800,000 3D meshes). 4DFAB contains recordings of 180 subjects captured in four different sessions spanning over a five-year period. It contains 4D videos of subjects displaying both spontaneous and posed facial behaviours. The database can be used for both face and facial expression recognition, as well as behavioural biometrics. It can also be used to learn very powerful blendshapes for parametrising facial behaviour. In this paper, we conduct several experiments and demonstrate the usefulness of the database for various applications. The database will be made publicly available for research purposes.

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A 3D Dynamic Database for Unconstrained Face Recognition

Cited by 8 publications

References 15 publications

Capture, Learning, and Synthesis of 3D Speaking Styles

Capture, Learning, and Synthesis of 3D Speaking Styles

Visual Methods for Sign Language Recognition: A Modality-Based Review

4DFAB: A Large Scale 4D Database for Facial Expression Analysis and Biometric Applications

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