Combined Face Detection/Recognition System for Smart Rooms

Author(s):  
Jia Kui ◽  
Liyanage C. De Silva
2014 ◽  
Vol 971-973 ◽  
pp. 1710-1713
Author(s):  
Wen Huan Wu ◽  
Ying Jun Zhao ◽  
Yong Fei Che

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.


2014 ◽  
Author(s):  
Jing Jin ◽  
Yuanqing Wang ◽  
Liujing Xu ◽  
Liqun Cao ◽  
Lei Han ◽  
...  

2014 ◽  
Vol 635-637 ◽  
pp. 985-988
Author(s):  
Wei Bo Yu ◽  
Lin Zhao ◽  
Wei Ming He

Because of the influence of complex image background, illumination changes, facial rotation and some other factors, makes face detection in complex background is much more difficult, lower accuracy and slower speed. Adaboost algorithm was used for face detection, and implemented the test process in OpenCV. Face detection experiments were performed on images with facial rotation and complex background, the detection accuracy rate was 85% and 99% respectively, the average detection time of each picture was 16.67ms and 76ms.Experimental results show that the face detection algorithm can accurately and quickly realize face detection in complex background, and can satisfy the requirements of real-time face recognition system.


2014 ◽  
Vol 9 (10) ◽  
Author(s):  
Peiyi Shen ◽  
Liang Zhang ◽  
Juan Song ◽  
Hu Xu ◽  
Lianjie Qin ◽  
...  

1997 ◽  
Author(s):  
Manesh B. Shah ◽  
Nageswara S. V. Rao ◽  
Victor Olman ◽  
Edward C. Uberbacher ◽  
Reinhold C. Mann

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