3-D FACE MODELING AND ITS APPLICATIONS

1996 ◽  
Vol 10 (05) ◽  
pp. 491-520 ◽  
Author(s):  
THOMAS S. HUANG ◽  
LI-AN TANG
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Computer Animation ◽  
Video Sequence ◽  
Human Face ◽  
Face Modeling ◽  
Human Face Recognition ◽  
Preliminary Results ◽  
Human Faces ◽  
Sequence Compression

This paper describes some issues in building a 3-D human face modeling system which mainly consists of three parts: • Modeling human faces; • Analyzing facial motions; • Synthesizing facial expressions. A variety of techniques developed for this system are described in detail in this paper. Some preliminary results of applying this system to computer animation, video sequence compression and human face recognition are also shown.

Розроблення системи розпізнавання людських облич для відеоспостереження

2020 ◽  
pp. 57-66
Author(s):  
Ірина Курта ◽  
◽  
Андрій Лагун ◽  
Keyword(s):  
Face Recognition ◽  
Recognition Algorithm ◽  
Software System ◽  
Human Face ◽  
Human Face Recognition ◽  
Building Systems ◽  
Recognition Of Objects ◽  
Human Faces

In the article were researched the principles of building systems for observation and recognition of objects. Also we have given the classification of human faces recognition methods. Authors have analized the features of operetion for the progressive calibration network (PCN) for human face recognition. And finally has been created and tested the developed face recognition algorithm as the realized software system.

scholarly journals Robot Face Memorability is Affected by Uncanny Appearance

2021 ◽  
Author(s):  
Allie Geiger ◽  
Benjamin Balas
Keyword(s):  
Face Recognition ◽  
Face Processing ◽  
Memory Task ◽  
Human Robot Interaction ◽  
Study Phase ◽  
Human Face ◽  
Human Face Recognition ◽  
Social Agents ◽  
Face Images ◽  
Human Faces

Human face recognition is influenced by various social and environmental constructs. For example, both age and race can affect the likelihood that a human face will be correctly recalled. Interestingly, general face appearance (i.e. friendly or untrustworthy faces) can also influence memorability. As human-robot interaction (HRI) becomes more commonplace, understanding what factors influence face recognition for non-human social agents is increasingly important. In particular, while there is a growing literature comparing the recognition of real human faces to computer-generated face images, comparisons between human face processing and robot face processing are largely unexplored. Here, we examined how the uncanny/eeriness of robot-faces affects memorability by using a 2AFC old/new task with various robot faces. Participants rated robot and human faces on perceived uncanniness during a study phase and were subsequently given a surprise memory task with only a fraction of the previously-encountered robot faces. Our results suggest that robots who are rated as more uncanny are more memorable than those that do not elicit the eerie feelings that correspond with uncanny faces: The more uncanny the robot face, the more accurately and quickly they were recalled. We discuss these results in the context of the design of social agents for HRI and also vis-a-vis theories of human face recognition and memory.

scholarly journals Communication and Automatic Interpretation of Affect from Facial Expressions

2011 ◽  
pp. 157-183 ◽  
Author(s):  
Albert Ali Salah ◽  
Nicu Sebe ◽  
Theo Gevers
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Computer Analysis ◽  
Computer Processing ◽  
Human Face ◽  
Human Face Recognition ◽  
Automatic Interpretation ◽  
Recent Advances ◽  
Realistic View ◽  
Near Future

The objective of this chapter is to introduce the reader to the recent advances in computer processing of facial expressions and communicated affect. Human facial expressions have evolved in tandem with human face recognition abilities, and show remarkable consistency across cultures. Consequently, it is rewarding to review the main traits of face recognition in humans, as well as consolidated research on the categorization of facial expressions. The bulk of the chapter focuses on the main trends in computer analysis of facial expressions, sketching out the main algorithms and exposing computational considerations for different settings. The authors then look at some recent applications and promising new projects to give the reader a realistic view of what to expect from this technology now and in near future.

The Adaboost Algorithm Applied in ATM Automatic Identification System

2013 ◽  
Vol 753-755 ◽  
pp. 2941-2944
Author(s):  
Ming Hui Zhang ◽  
Yao Yu Zhang
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Computing Time ◽  
Automatic Identification ◽  
Identification System ◽  
Human Face ◽  
Detection Experiment ◽  
Adaboost Algorithm ◽  
Face Features ◽  
Human Faces

Seeing that human face features are unique, an increasing number of face recognition algorithms on existing ATM are proposed. Since face detection is a primary link of face recognition, our system adopts AdaBoost algorithm which is based on face detection. Experiment results demonstrated that the computing time of face detection using this algorithm is about 70ms, and the single and multiple human faces can be effectively measured under well environment, which meets the demand of the system.

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