scholarly journals Pose-Invariant Face Recognition via RGB-D Images

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Gaoli Sang ◽  
Jing Li ◽  
Qijun Zhao
Keyword(s):  
Face Recognition ◽  
Similarity Measure ◽  
Three Dimensional ◽  
Recognition Problem ◽  
Depth Information ◽  
Two Dimensional ◽  
Recognition Method ◽  
3D Face

Three-dimensional (3D) face models can intrinsically handle large pose face recognition problem. In this paper, we propose a novel pose-invariant face recognition method via RGB-D images. By employing depth, our method is able to handle self-occlusion and deformation, both of which are challenging problems in two-dimensional (2D) face recognition. Texture images in the gallery can be rendered to the same view as the probe via depth. Meanwhile, depth is also used for similarity measure via frontalization and symmetric filling. Finally, both texture and depth contribute to the final identity estimation. Experiments on Bosphorus, CurtinFaces, Eurecom, and Kiwi databases demonstrate that the additional depth information has improved the performance of face recognition with large pose variations and under even more challenging conditions.

scholarly journals Do 3D Face Images Capture Cues of Strength, Weight, and Height Better than 2D Face Images do?

2021 ◽  
Vol 7 (3) ◽  
pp. 209-219
Author(s):  
Iris J Holzleitner ◽  
Alex L Jones ◽  
Kieran J O’Shea ◽  
Rachel Cassar ◽  
Vanessa Fasolt ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Physical Characteristics ◽  
Two Dimensional ◽  
3D Images ◽  
3D Face ◽  
Face Images ◽  
Similar Accuracy ◽  
2D And 3D ◽  
2D Images ◽  
Better Than

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.

3D Face Recognition Based on Symbolic FDA Using SVM Classifier with Similarity and Dissimilarity Distance Measure

2017 ◽  
Vol 31 (04) ◽  
pp. 1756006 ◽  
Author(s):  
Manjunatha Hiremath ◽  
P. S. Hiremath
Keyword(s):  
Face Recognition ◽  
Distance Measure ◽  
Svm Classifier ◽  
Depth Information ◽  
3D Face Recognition ◽  
Emotional States ◽  
Range Images ◽  
Human Face ◽  
Biometric Technology ◽  
3D Face

Human face images are the basis not only for person recognition, but for also identifying other attributes like gender, age, ethnicity, and emotional states of a person. Therefore, face is an important biometric identifier in the law enforcement and human–computer interaction (HCI) systems. The 3D human face recognition is emerging as a significant biometric technology. Research interest into 3D face recognition has increased during recent years due to availability of improved 3D acquisition devices and processing algorithms. A 3D face image is represented by 3D meshes or range images which contain depth information. In this paper, the objective is to propose a new 3D face recognition method based on radon transform and symbolic factorial discriminant analysis using KNN and SVM classifier with similarity and dissimilarity measures, which are applied on 3D facial range images. The experimentation is done using three publicly available databases, namely, Bhosphorus, Texas and CASIA 3D face database. The experimental results demonstrate the effectiveness of the proposed method.

scholarly journals 3D Face Recognition Method Based on Cascade Classifier

2012 ◽  
Vol 29 ◽  
pp. 705-709 ◽  
Author(s):  
Bi Kun ◽  
Luo Lin ◽  
Zhao Li ◽  
Fang Shi Liang
Keyword(s):  
Face Recognition ◽  
3D Face Recognition ◽  
Cascade Classifier ◽  
Recognition Method ◽  
3D Face

Sketching in three dimensions: A beautification scheme

2016 ◽  
Vol 31 (3) ◽  
pp. 376-392 ◽  
Author(s):  
Sree Shankar ◽  
Rahul Rai
Keyword(s):  
Three Dimensional ◽  
Digital Design ◽  
User Studies ◽  
Human Interaction ◽  
Three Dimensions ◽  
Depth Information ◽  
Two Dimensional ◽  
Depth Sensing ◽  
User Input ◽  
Making Sense

AbstractPrimary among all the activities involved in conceptual design is freehand sketching. There have been significant efforts in recent years to enable digital design methods that leverage humans’ sketching skills. Conventional sketch-based digital interfaces are built on two-dimensional touch-based devices like sketchers and drawing pads. The transition from two-dimensional to three-dimensional (3-D) digital sketch interfaces represents the latest trend in developing new interfaces that embody intuitiveness and human–human interaction characteristics. In this paper, we outline a novel screenless 3-D sketching system. The system uses a noncontact depth-sensing RGB-D camera for user input. Only depth information (no RGB information) is used in the framework. The system tracks the user's palm during the sketching process and converts the data into a 3-D sketch. As the generated data is noisy, making sense of what is sketched is facilitated through a beautification process that is suited to 3-D sketches. To evaluate the performance of the system and the beautification scheme, user studies were performed on multiple participants for both single-stroke and multistroke sketching scenarios.

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