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HCI Final Project Robust Real Time Face Detection Paul Viola, Michael Jones, Robust RealTime Face De

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Title: HCI Final Project Robust Real Time Face Detection Paul Viola, Michael Jones, Robust RealTime Face De


1
HCI Final ProjectRobust Real Time Face
DetectionPaul Viola, Michael Jones, Robust
Real-Time Face Detetion, International Journal of
Computer Vision, 2004.
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2
Goals
  • Real Time Computation
  • High Detection Rate

Face Detector
Input Image
Output Image
3
Why Do We Care About Speed?
  • Robotics need a real-time face detector.
  • Thousands upon thousands of images in the album
  • Animal vision systems are both fast and accurate
  • Security system

4
Abstract
  • A fast face detection framework in color image
    combining a skin color detector and Viola-Jones
    Detector (VJD)
  • Skin segmentation
  • Normalized Color Coordinates (NCC) color space
  • Morphology operations remove the noises
  • Viola-Jones Detector (VJD) performs face
    detection only in the segmented skin color
    regions
  • We built the proposed system using OpenCV
  • Results showed that the detection time of
    proposed algorithm can yield an improvement of
    more than 50 percent, compared with that of
    Viola-Jones Detector (VJD), while resulting in
    lower false alarm rate.

5
The Viola-Jones Detector (VJD)
  • Haar-like features
  • Integral image a fast way to compute simple
    features
  • In Adaboost the weak learner is nothing but a
    feature selector. The advantage is that if there
    are N weak learners there are merely N features
    to compute.
  • Cascaded combination of classifiers. Most of true
    negatives are rejected very fast at the at the
    first few stages. Can keep high detection rate
    and low false positive rate.

6
Haar-like Features
  • Four basic types.
  • They are easy to calculate.
  • The white areas are subtracted from the black
    ones.
  • A special representation of the sample called the
    integral image makes feature extraction faster.
  • Features are extracted from sub windows of an
    sample image.
  • The base size for a sub window is 24 by 24
    pixels.
  • In a 24 pixel by 24 pixel sub window there are
    180,000 possible features to be calculated.

7
Integral images
  • Summed area tables
  • A representation that means any rectangles area
    can be calculated in four indexes to the integral
    image.

8
Cascade classifier
  • Selects a small number of critical visual
    features
  • Combines a collection of weak classification
    functions to form a strong classifier
  • Stages in the cascade are constructed by training
    classifiers using Adaboost
  • Training this detector takes weeks but it is done
    once and for all. Then, it processes 15 frames
    per second

9
Advantages using OpenCV Haar object detection
  • Face detector already implemented
  • Its only argument is a xml file
  • Detection at any scale
  • Face detection at 15 frames per second for
    384288 pixel images
  • 90 objects detected
  • 10-6 false positive rate (FAR)

10
Skin segmentation
  • Face-like regions ? skin-like regions
  • A simple, efficient, and rapid method
  • Normalized Color Coordinates (NCC)

11
Proposed scheme
  • Skin segmentation
  • Binarization
  • Morphology operation
  • VJD using OpenCV

12
Results and Discussions
  • Two systems, VJD and the proposed face detection
    system, were implemented using Visual C 6.0
    with OpenCV on a Pentium(R) Core 2 Dual _at_ 2.00
    GHz PC with 2.00 GB RAM.
  • For conducting experiments, some materials shown
    in Table 1 and Table 2 were searched on Google
    Image Search Website, and some captured by a
    digital camera were from our lab. The
    experimental images are in size of from 400x464
    pixels image for web display to 2186x2112 pixels
    image captured by a digital camera. Totally there
    are 26 images.
  • For face detection time evaluation, the proposed
    system and VJD performed 1000 times repeatedly on
    every image. Finally, pick the minimum detection
    time as the fast detection time for experimental
    comparison.
  • The proposed system improved detection time for
    color image, and not only kept the robust
    property, but also decreased false alarm rate and
    false rejection rate.

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Conclusions
  • The proposed system is faster than VJD in color
    image.
  • This research demonstrated that a skin color
    detection unit as preprocessing can improve the
    detection time of VJD in a color image. This idea
    can be extended to other problems, e.g. hand
    detection. If we know the color conditions in
    that problem in advance, color information is
    helpful and useful to improve the detection time.
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