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

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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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Goals

• Real Time Computation
• High Detection Rate

Face Detector
Input Image
Output Image
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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

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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.

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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.

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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.

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Integral images

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

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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

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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)

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Skin segmentation

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

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Proposed scheme

• Skin segmentation
• Binarization
• Morphology operation
• VJD using OpenCV

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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.