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Face-Detection using Maximal Rejection Classification and Color Techniques

Face-Detection using Maximal Rejection Classification and Color Techniques. Group 11 Sam Mazin & Priti Balchandani. Motivation. Michael Elad’s presentation on rejection-based techniques Felt a linear classifier (MRC) would be fastest and thus most practical in real-world applications.

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Face-Detection using Maximal Rejection Classification and Color Techniques

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  1. Face-Detection using Maximal Rejection Classification and Color Techniques Group 11 Sam Mazin & Priti Balchandani

  2. Motivation • Michael Elad’s presentation on rejection-based techniques • Felt a linear classifier (MRC) would be fastest and thus most practical in real-world applications

  3. High-level Design Maximal Rejection Classifier (MRC) Color Rejection Morphological Processing FACES

  4. Maximal Rejection Background Training: Backgrounds d1 Faces (Convex) d2 θ

  5. Maximal Rejection Classifying: Background d1n ? MRC Projection 15x15 Test Block Last θ Face Not last θ: Project with θn+1 d2n Very fast θn

  6. Color Rejection • MRC was not the greatest on its own; many false positives remained • Tried RGB segmentation  not great • Y-Cb-Cr space showed promise • Decided to take mean Cb and Cr values of all 15x15 blocks (ignored Y to avoid intensity bias)

  7. Color Rejection using FLD

  8. Optimal Cb-Cr FLD Projection Projection Density: p(w) Faces Backgrounds w W*=max(FLD_proj(Faces))

  9. Morphological Processing Maximal Rejection Classifier (MRC) Color Rejection Morphological Processing FACES

  10. Morphological Processing • Combined 3 resolution levels into one • Dilated and performed centroid search to get rid of repeated face detections

  11. Finally…

  12. Desperate times… • Monday night, 10pm • Still had 5-6 false positives popping up • Decided to implement the “Look Down Method” • Reran tests: made good scores better, but bad scores worse

  13. Conclusion • MRC is fast but not 100% reliable (probably due to lack of data) • Color rejection helped significantly, Cb-Cr good means of classification • Morphological processing necessary for repeated detections • Spent too much time tweaking the MRC

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