DocumentCode
596810
Title
A highly parallelized processor for face detection based on Haar-like features
Author
Huabiao Qin ; Lianbing Tian ; Zongwei Hu
Author_Institution
Sch. of Electron. & Inf. Eng., South China Univ. of Technol., Guangzhou, China
fYear
2012
fDate
9-12 Dec. 2012
Firstpage
985
Lastpage
988
Abstract
This paper presents a hardware architecture for face detection based on the Viola and Jones object detection method. To achieve high speed detection, two major features are introduced in this paper, including rapid integral image generation and hybrid stage classifier. A register array structure is utilized for storing and generating integral image, facilitating the simultaneous access and parallel classifier evaluation. Moreover, a hybrid stage classifier is proposed which combines both serial processing and parallel processing, making the best trade-off between detection speed and resource consumption. In addition, the detection system can detect human faces in a 384×288 image at a speed of 22 fps when the specialized processor in a Stratix II FPGA works at 100 MHz.
Keywords
Haar transforms; face recognition; field programmable gate arrays; image classification; microprocessor chips; object detection; Haar-like features; Stratix II FPGA; detection speed; detection system; face detection; frequency 100 MHz; hardware architecture; high speed detection; highly parallelized processor; hybrid stage classifier; object detection method; parallel classifier evaluation; parallel processing; rapid integral image generation; register array structure; resource consumption; serial processing; Algorithm design and analysis; Computer architecture; Face; Face detection; Feature extraction; Field programmable gate arrays; Hardware;
fLanguage
English
Publisher
ieee
Conference_Titel
Electronics, Circuits and Systems (ICECS), 2012 19th IEEE International Conference on
Conference_Location
Seville
Print_ISBN
978-1-4673-1261-5
Electronic_ISBN
978-1-4673-1259-2
Type
conf
DOI
10.1109/ICECS.2012.6463524
Filename
6463524
Link To Document