• 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