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D.George,S.Rane : Stanford University EE 368 Project Presentation, May 28, 2002

D.George,S.Rane : Stanford University EE 368 Project Presentation, May 28, 2002. Face Detection using Fisher Linear Discriminant with A Cross Trained Neural Network (A Multi-Algorithm Approach) Dileep George & Shantanu Rane [ EE 368 Class Project, Spring 2002 ].

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D.George,S.Rane : Stanford University EE 368 Project Presentation, May 28, 2002

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  1. D.George,S.Rane : Stanford University EE 368 Project Presentation, May 28, 2002 Face Detection using Fisher Linear Discriminant with A Cross Trained Neural Network (A Multi-Algorithm Approach) Dileep George & Shantanu Rane [EE 368 Class Project, Spring 2002]

  2. The Two Category Classification Problem Non Ideal Classifier Ideal Classifier N-D Space D.George,S.Rane : Stanford University EE 368 Project Presentation, May 28, 2002

  3. ALGO 1 ALGO 2 ALGO 3 D.George,S.Rane : Stanford University EE 368 Project Presentation, May 28, 2002 The Multi-algorithm approach • Several classification algorithms in pipeline • Different algorithms map N-D space differently to the real line • Thresholds set such that no faces are rejected.

  4. D.George,S.Rane : Stanford University EE 368 Project Presentation, May 28, 2002 Multi-algorithm Advantages • Shrinks search space for subsequent algorithms • Reduced computational complexity • Algorithm Parameters can be tuned to the False Alarm Space of its predecessor.

  5. D.George,S.Rane : Stanford University EE 368 Project Presentation, May 28, 2002 Algorithm 1: Skin Color Segmentation • YUV Space : Mean Cb, Mean Cr, Cov CbCr • Thresholding mechanism • Number of Operations =~ 1 per pixel. • Followed by Morphological Operations Search-space reduced by a factor of 0.5 [Chang, Robles, EE368 Class Project, Spr 2000]

  6. D.George,S.Rane : Stanford University EE 368 Project Presentation, May 28, 2002 Algorithm 2: Fischer Linear Discriminant • Finds best direction of projection w for maximal separation. • 1 convolution per pixel [Duda, Hart, Stork, 2002]

  7. D.George,S.Rane : Stanford University EE 368 Project Presentation, May 28, 2002 Search Space After FLD Search-space reduced by a factor of 0.0062

  8. Mahalanobis Distance • Approximated as sum of two components • DIFS • DFFS • Eigenfaces • 8 convolutions per pixel [Moghaddam, Pentland,1997] D.George,S.Rane : Stanford University EE 368 Project Presentation, May 28, 2002 Algorithm 3: Mahalanobis Distance

  9. D.George,S.Rane : Stanford University EE 368 Project Presentation, May 28, 2002 Search Space After Mahalanobis Distance Operation Search-space reduced by a factor of 0.0037

  10. 700 400 D.George,S.Rane : Stanford University EE 368 Project Presentation, May 28, 2002 Algorithm 4: Neural Network • Non-linear technique • High complexity - 300 convolutions per pixel. • Gradient Descent with Momentum • Boot-strapping • Cross Algorithm Training

  11. D.George,S.Rane : Stanford University EE 368 Project Presentation, May 28, 2002 Reduced Computational Complexity Sample Calculations for a 400-700-1 Neural Network

  12. D.George,S.Rane : Stanford University EE 368 Project Presentation, May 28, 2002 Special Cases • Texture Detection • Multi-scale Operation • Hard-to-Detect Faces • False Alarm Reduction

  13. D.George,S.Rane : Stanford University EE 368 Project Presentation, May 28, 2002 Results

  14. D.George,S.Rane : Stanford University EE 368 Project Presentation, May 28, 2002 Conclusion • Scale Sensitivity • Representation Vs Discrimination • Cross Algorithm Training • Size of Training Set

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