Analysing Facial Features Using CNNs and Computer Vision

2020 ◽  
pp. 146-157
Author(s):  
Diana Borza ◽  
Razvan Itu ◽  
Radu Danescu ◽  
Ioana Barbantan
Keyword(s):  
Computer Vision ◽  
Facial Features

A 3D Dynamic Face Recognition Method Based on Computer Vision

2014 ◽  
Vol 556-562 ◽  
pp. 5006-5008 ◽  
Author(s):  
Bo Xia Zeng ◽  
Wen Feng Li
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Traditional Method ◽  
Recognition Algorithm ◽  
Facial Features ◽  
Nonlinear Mapping ◽  
Geometric Features ◽  
Feature Points ◽  
Recognition Method ◽  
2D Images

The non-rigid 3D characters recovery technology for 2D images array is affected by background diversity, motion complexity, data losing and noise of feature points, so the recognition and recovery accuracy of facial features deformation is low. Due to the high error in traditional method, the paper puts forward a 3D facial recognition algorithm based on random images array, which converts the 2D features to 3D by nonlinear mapping, and completes the recognition on foundation of 3D geometric features distance. The experimental results show that the method effectively reduces error and improves recognition effects.

scholarly journals A survey on computer vision techniques for detecting facial features towards the early diagnosis of mild cognitive impairment in the elderly

2019 ◽  
Vol 7 (1) ◽  
pp. 252-263
Author(s):  
Zixiang Fei ◽  
Erfu Yang ◽  
David Day-Uei Li ◽  
Stephen Butler ◽  
Winifred Ijomah ◽  
...  
Keyword(s):  
Computer Vision ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Diagnosis ◽  
The Elderly ◽  
Facial Features

scholarly journals Generalized Omnipresence Detection (GOD)

2020 ◽  
Vol 2 (2) ◽  
pp. 85-92
Author(s):  
Haretha Winmalar D ◽  
Vani A K ◽  
Sudharsan R ◽  
Hari Krishna R
Keyword(s):  
Computer Vision ◽  
Local Binary Pattern ◽  
Depth Information ◽  
Facial Features ◽  
Human Face ◽  
Detection And Tracking ◽  
Illumination Changes ◽  
Human Object ◽  
And Behavior ◽  
Pattern Histogram

Identification and Tracking of a person in a video are useful in applications such as video surveillance. Two levels of tracking are carried out. They are Classification and monitoring of individuals. The human body’s color histogram is used as the basis for monitoring individuals. Our project can detect a human face in a video and store the detected facial features of the Local Binary Pattern Histogram (LBPH). In a video, once a person is detected, it automatically track that individual and assigns a label to that individual. We use the stored LBPH features to track him in any other videos. In this paper, we proposed and compared the efficiency of two algorithms. One constantly updates the background to make it suitable for illumination changes and other uses depth information with RGB. This is the first step in many complex algorithms in computer vision, such as identification of human activity and behavior recognition. The main challenges in human/object detection and tracking are changing illumination and background. Our work is based on image processing and also it learns the activities and stores them using machine learning with the help of OpenCV, an open source computer vision library.

Computational Modeling of Face Recognition Based on Psychophysical Experiments

2004 ◽  
Vol 63 (3) ◽  
pp. 207-215 ◽  
Author(s):  
Adrian Schwaninger ◽  
Christian Wallraven ◽  
Heinrich H. Bülthoff
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Cognitive Processes ◽  
Face Processing ◽  
Recognition Performance ◽  
Interdisciplinary Approach ◽  
Striking Similarity ◽  
Facial Features ◽  
Configural Information ◽  
Psychophysical Studies

Recent results from psychophysical studies are discussed which clearly show that face processing is not only holistic. Humans do encode face parts (component information) in addition to information about the spatial interrelationship of facial features (global configural information). Based on these findings we propose a computational architecture of face recognition, which implements a component and configural route for encoding and recognizing faces. Modeling results showed a striking similarity between human psychophysical data and the computational model. In addition, we could show that our framework is able to achieve good recognition performance even under large view rotations. Thus, our study is an example of how an interdisciplinary approach can provide a deeper understanding of cognitive processes and lead to further insights in human psychophysics as well as computer vision.

Inner and Outer Eye Corners Detection for Facial Features Extraction Based on CTGF Algorithm

2011 ◽  
Vol 58-60 ◽  
pp. 1966-1971 ◽  
Author(s):  
Ran Zhou ◽  
Qing He ◽  
Jie Wu ◽  
Chao Hu ◽  
Q. H. Meng
Keyword(s):  
Computer Vision ◽  
Detection Method ◽  
Facial Feature ◽  
Features Extraction ◽  
Facial Features ◽  
Texture Information ◽  
Polar Coordinate ◽  
Computer Vision Applications

This paper proposes a novel inner and outer eye corners detection method, which inosculates corner, regional texture and gray information, called CTGF algorithm. It utilizes corner detector to determine the candidate points of eye corners, such as Harris. Next, the region texture information is obtained through polar coordinate integral, in order to locate the exactly positions of eye corners among the candidate points. The CTGF algorithm provides a precise and reliable facial feature for many computer vision applications and the robustness and accuracy are demonstrated in experiments.

The Impact of Visual Stereotypes on Judgments about Rape

2016 ◽  
Vol 75 (3) ◽  
pp. 133-140
Author(s):  
Robert Busching ◽  
Johannes Lutz
Keyword(s):  
Mental Representations ◽  
Irrelevant Information ◽  
Clear Distinction ◽  
Rape Myth ◽  
Facial Features ◽  
Reverse Correlation ◽  
Correlation Technique ◽  
Specific Effects ◽  
Legal Judgments ◽  
The Impact

Abstract. Legally irrelevant information like facial features is used to form judgments about rape cases. Using a reverse-correlation technique, it is possible to visualize criminal stereotypes and test whether these representations influence judgments. In the first step, images of the stereotypical faces of a rapist, a thief, and a lifesaver were generated. These images showed a clear distinction between the lifesaver and the two criminal representations, but the criminal representations were rather similar. In the next step, the images were presented together with rape scenarios, and participants (N = 153) indicated the defendant’s level of liability. Participants with high rape myth acceptance scores attributed a lower level of liability to a defendant who resembled a stereotypical lifesaver. However, no specific effects of the image of the stereotypical rapist compared to the stereotypical thief were found. We discuss the findings with respect to the influence of visual stereotypes on legal judgments and the nature of these mental representations.

Human Vision and Computer Vision

1985 ◽  
Vol 30 (1) ◽  
pp. 47-47
Author(s):  
Herman Bouma
Keyword(s):  
Computer Vision ◽  
Human Vision

Face-ing who you are: Do racially prototypic facial features predict racial identification?

2014 ◽  
Author(s):  
Kathy Espino-Perez ◽  
Ryan Folliott ◽  
Brandon K. Brown ◽  
Debbie S. Ma
Keyword(s):  
Facial Features ◽  
Racial Identification
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