Real-time multiple face detection using active illumination

2002 ◽  
Author(s):  
C.H. Morimoto ◽  
M. Flickner
Keyword(s):  
Real Time ◽  
Face Detection ◽  
Active Illumination

Research and Implementation of Face Detection Based on OpenCV

2014 ◽  
Vol 971-973 ◽  
pp. 1710-1713
Author(s):  
Wen Huan Wu ◽  
Ying Jun Zhao ◽  
Yong Fei Che
Keyword(s):  
Face Recognition ◽  
Detailed Analysis ◽  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Accuracy Rate ◽  
Face Recognition System ◽  
The Face

Face detection is the key point in automatic face recognition system. This paper introduces the face detection algorithm with a cascade of Adaboost classifiers and how to configure OpenCV in MCVS. Using OpenCV realized the face detection. And a detailed analysis of the face detection results is presented. Through experiment, we found that the method used in this article has a high accuracy rate and better real-time.

A ROBUST AND FRIENDLY HUMAN–ROBOT INTERFACE SYSTEM BASED ON NATURAL HUMAN GESTURES

2010 ◽  
Vol 24 (06) ◽  
pp. 847-866 ◽  
Author(s):  
MING-SHAUNG CHANG ◽  
JUNG-HUA CHOU
Keyword(s):  
Real Time ◽  
Face Detection ◽  
Fuzzy Logic Controller ◽  
Tracking System ◽  
Back Propagation ◽  
Principal Component ◽  
Working Environment ◽  
Expression Recognition ◽  
Linear Discriminant ◽  
Human Gestures

In this paper, we design a robust and friendly human–robot interface (HRI) system for our intelligent mobile robot based only on natural human gestures. It consists of a triple-face detection method and a fuzzy logic controller (FLC)-Kalman filter tracking system to check the users and predict their current position in a dynamic and cluttered working environment. In addition, through the combined classifier of the principal component analysis (PCA) and back-propagation artificial neural network (BPANN), single and successive commands defined by facial positions and hand gestures are identified for real-time command recognition after dynamic programming (DP). Therefore, the users can instruct this HRI system to make member recognition or expression recognition corresponding to their gesture commands, respectively based on the linear discriminant analysis (LDA) and BPANN. The experimental results prove that the proposed HRI system perform accurately in real-time face detection and tracking, and robustly react to the corresponding gesture commands at eight frames per second (fps).

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