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Automated detection of faces in images

Automated detection of faces in images. Sameer Jain Mehdi Mohseni Joy Rajiv. Flow Chart. Neural network. Skin color Based filtering. FLD. &. Regions in doubt. Low threshold Some non faces. +. Template matching. -. faces. +. High threshold All faces. Color Based Filtering.

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Automated detection of faces in images

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  1. Automated detection of faces in images Sameer Jain Mehdi Mohseni Joy Rajiv

  2. Flow Chart Neural network Skin color Based filtering FLD & Regions in doubt Low threshold Some non faces + Template matching - faces + High threshold All faces

  3. Color Based Filtering V S H C. Garcia et al, IEEE Trans. Multim. 1999 • Hue Saturation Intensity space • Trained for face colors (8 vector) • Trained for non-face colors (32 vectors)

  4. Template Matching • Template of 6 sizes created (needed only smallest and 5th largest) • Segmented image into 2 halfs: • Top half smaller template • Bottom half larger template • Combined result • High threshold: miss some faces • Low threshold: get false hits

  5. Fisher Linear Discriminant • Procedure described in class • Worked on the template matching result with a lower threshold • Still see some false hits • Limitation due to: • Small training set • Face and non face not linearly separable

  6. w1 f1 w0 w2 f2 + +1:face +1 -1 -1: non face en=dn-wnHfn wn2 fn2 wn+1=wn-uenfn Neural network • Trained the system: • -1 for a non face • +1 for a face • Again saw some false hits • Limited success due to small training set

  7. Final Result of Routine • Combined results of FLD and neural network • Detect all faces in 4/7 images (~96% accuracy) • Approx time (~20s)

  8. Conclusion • Used • Color information • Shape information (template matching) • FLD • neural network approach • Use fact that the image set is limited • Fast algorithm (~20s) • Accurate algorithm (96% accuracy) on given test images

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