Qualitative study on 3D face databases: A review

Dharavath, Krishna; Laskar, Rabul Hussain; Talukdar, Fazal A.

doi:10.1109/indcon.2013.6726093

Cited by 4 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous techniques can be used to create a mesh consisting of triangles, quadrilaterals, or other simple convex polygons from a point cloud, with the power crust algorithm standing out as the most effective. Furthermore, Dharavath et al [162] describe a technique for constructing a regular facial mesh model based on the scattered point cloud.…”

Section: B 3d Datamentioning

confidence: 99%

Multimodal Face Data Sets—A Survey of Technologies, Applications, and Contents

Al-Mannai,

Al-Thelaya,

Schneider

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Due to their ease-of-use, biometric verification methods to control access to digital devices have become ubiquitous. Many rely on supervised machine learning, a process that is notoriously datahungry. At the same time, biometric data is sensitive from a privacy perspective, and a comprehensive review from a data set perspective is lacking. In this survey, we present a comprehensive review of multimodal face data sets (e.g., data sets containing RGB color plus other channels such as infrared or depth). This follows a trend in both industry and academia to use such additional modalities to improve the robustness and reliability of the resulting biometric verification systems. Furthermore, such data sets open the path to a plethora of additional applications, such as 3D face reconstruction (e.g., to create avatars for VR and AR environments), face detection, registration, alignment, and recognition systems, emotion detection, anti-spoofing, etc. We also provide information regarding the data acquisition setup and data attributes (ethnicities, poses, facial expressions, age, population size, etc.) as well as a thorough discussion of related applications and state-of-the-art benchmarking. Readers may thus use this survey as a tool to navigate the existing data sets both from the application and data set perspective. To existing surveys we contribute, to the best of our knowledge, the first exhaustive review of multimodalities in these data sets.

show abstract

Section: B 3d Datamentioning

confidence: 99%

Multimodal Face Data Sets—A Survey of Technologies, Applications, and Contents

Al-Mannai,

Al-Thelaya,

Schneider

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Apart from these, recent research focuses on building 3D facial statistical models on a large collection of 10,000 static faces [14], and facial expressions in the wild [10]. As our focus is to develop a 3D dynamic (4D) facial dataset for challenging elastic deformation, we would refer readers to recent surveys and latest work in [15,16].…”

Section: Datasets For 3d Non-rigid Surface Registrationmentioning

confidence: 99%

HDFD --- A High Deformation Facial Dynamics Benchmark for Evaluation of Non-Rigid Surface Registration and Classification

Andrews,

Endean,

Dyke

et al. 2018

Preprint

View full text Add to dashboard Cite

Objects that undergo non-rigid deformation are common in the real world. A typical and challenging example is the human faces. While various techniques have been developed for deformable shape registration and classification, benchmarks with detailed labels and landmarks suitable for evaluating such techniques are still limited. In this paper, we present a novel facial dynamic dataset HDFD which addresses the gap of existing datasets, including 4D funny faces with substantial non-isometric deformation, and 4D visual-audio faces of spoken phrases in a minority language (Welsh). Both datasets are captured from 21 participants. The sequences are manually landmarked, with the spoken phrases further rated by a Welsh expert for level of fluency. These are useful for quantitative evaluation of both registration and classification tasks. We further develop a methodology to evaluate several recent non-rigid surface registration techniques, using our dynamic sequences as test cases. The study demonstrates the significance and usefulness of our new dataset -a challenging benchmark dataset for future techniques.

show abstract