Influence of Location over Several Classifiers in 2D and 3D Face Verification

Author(s):  
Susana Mata ◽  
Cristina Conde ◽  
Araceli Sánchez ◽  
Enrique Cabello
Keyword(s):  
3D Face ◽  
2018 ◽  
Vol 49 (4) ◽  
pp. 1339-1354 ◽  
Author(s):  
Mohcene Bessaoudi ◽  
Mebarka Belahcene ◽  
Abdelmalik Ouamane ◽  
Ammar Chouchane ◽  
Salah Bourennane

2017 ◽  
Vol 62 ◽  
pp. 68-80 ◽  
Author(s):  
Abdelmalik Ouamane ◽  
Elhocine Boutellaa ◽  
Messaoud Bengherabi ◽  
Abdelmalik Taleb-Ahmed ◽  
Abdenour Hadid

2021 ◽  
Vol 7 (3) ◽  
pp. 209-219
Author(s):  
Iris J Holzleitner ◽  
Alex L Jones ◽  
Kieran J O’Shea ◽  
Rachel Cassar ◽  
Vanessa Fasolt ◽  
...  

Abstract Objectives A large literature exists investigating the extent to which physical characteristics (e.g., strength, weight, and height) can be accurately assessed from face images. While most of these studies have employed two-dimensional (2D) face images as stimuli, some recent studies have used three-dimensional (3D) face images because they may contain cues not visible in 2D face images. As equipment required for 3D face images is considerably more expensive than that required for 2D face images, we here investigated how perceptual ratings of physical characteristics from 2D and 3D face images compare. Methods We tested whether 3D face images capture cues of strength, weight, and height better than 2D face images do by directly comparing the accuracy of strength, weight, and height ratings of 182 2D and 3D face images taken simultaneously. Strength, height and weight were rated by 66, 59 and 52 raters respectively, who viewed both 2D and 3D images. Results In line with previous studies, we found that weight and height can be judged somewhat accurately from faces; contrary to previous research, we found that people were relatively inaccurate at assessing strength. We found no evidence that physical characteristics could be judged more accurately from 3D than 2D images. Conclusion Our results suggest physical characteristics are perceived with similar accuracy from 2D and 3D face images. They also suggest that the substantial costs associated with collecting 3D face scans may not be justified for research on the accuracy of facial judgments of physical characteristics.


2017 ◽  
Vol 12 (11) ◽  
pp. 2751-2762 ◽  
Author(s):  
Abdelmalik Ouamane ◽  
Ammar Chouchane ◽  
Elhocine Boutellaa ◽  
Mebarka Belahcene ◽  
Salah Bourennane ◽  
...  
Keyword(s):  

Author(s):  
Junquan Liu ◽  
Feipeng Da ◽  
Xing Deng ◽  
Yi Yu ◽  
Pu Zhang

Author(s):  
PEIJIANG LIU ◽  
YUNHONG WANG ◽  
ZHAOXIANG ZHANG

We propose a novel representation of 3D face shape which is a key step for feature extraction and face recognition. The input of the proposed methods is unstructured point cloud, which determines the wide applicability of the proposed representation. Our contributions mainly include two parts: Spherical Depth Map (SDM) and face alignment based on SDM. SDM, which can be adopted to many applications, is a special kind of range image utilizing the prior anatomical knowledge of human face. Useful characteristics of SDM facilitate face alignment with higher efficiency and accuracy. Experiments conducted on three popular 3D face databases verify the high efficacy and superiority of the proposed method. The accuracy of face alignment is up to 100% with our strategy. The face verification rates based on the standard protocols are all higher than the baseline performance of FRGC2.0.


2019 ◽  
Vol 14 (7) ◽  
pp. 1917-1927 ◽  
Author(s):  
Yi Yu ◽  
Feipeng Da ◽  
Yifan Guo
Keyword(s):  

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