Group 11 Sam Mazin & Priti Balchandani 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

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

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

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

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)

Color Rejection using FLD

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

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

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

Finally…

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

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