ECE738 Final Project Face Detection Baseline Tanaphol Thaipanich thaipanich@wiscmail.wisc.edu

Objectives Face localization High detection rate & Low false alarm Color Image with good lighting condition Upright Frontal (Pose, Rotation and Occlusion) Low / High resolution High detection rate & Low false alarm Fast performance (real-time)

Solution Skin color map Haar-like features with Boosted Cascade

Skin color map Douglas Chai and King N. Ngan (1999) Success rate of 82% Sanjay Kr. Singh, D. S. Chauhan, Mayank Vatsa and Richa Singh (2003) RGB(97), YCbCr(99) and HIS(96) Accuracy of 95.18%

Skin color map

Skin color map The chrominance values in facial region are narrowly distributed RCb = [77 127] and RCr = [133 173] Robust against different types of skin color Brightness is nonuniform throughout facial region Block size of 8x8 If SD <= 2 then it is unlikely to be facial region

Skin color map Density regulation Geometric Correction Erode and dilate Geometric Correction Remove isolate noise in background

Experiments

Experiments

Haar-like features with Boosted Cascade Over-complete Haar-like features Fast feature extraction Feature reduction – Adaboost Fast decision – Attentional Cascade

Over-complete Haar-like features Paul Viola and Michael Jones Window 24 x 24 Rainer Lienhart and Jochen Maydt More complex but yield better result

Over-complete Haar-like features Total = 162337 20736 features

Fast feature extraction Fast feature computation  Integral Image Integral Image at (x,y) = sum of all pixels above and to the left of (x,y) Ex) D = 4+1-(2+3) = A+B+C+D+A – (A+B+A+C)

Adaboost Combine small number of these features to form a strong classifier Idea: Each round of boosting select 1 feature  lowest classification error when perform alone After each round, re-adjust weight of training samples  weight more on misclassified data Create “Strong classifier” based on those features

Adaboost Example of the selected features by Adaboost

Attentional cascade Use simple classifier to reject the majority of sub-window before calling more complex classifier  Low false alarm Stage 1 Stage 2 Stage N Sub-Window Reject Sub-Window

Experimental results Training set Face database: AR, BioID, ORL and Yales  3871 face samples Noface (opposing class) database: practically unlimited

Experimental results Training process: Extremely time-consuming What they did: Use 4916 face samples and 350 million (Oh! My god) noface samples (from 9544 images) 38 stages with over 6000 features Detection rate = 95% and false alarm = 1 in 14084 Now, what we have: Use 250 face samples and 250 noface samples (randomly select from a pool of samples) 3 stages with 50 features Detection rate = 93.4% (miss 33 from 500) False alarm = 5.4% (false detect 27 from 500)

Experimental results Improvement Combine result from “Skin map detection” & speed-up using detection map (Multiple detection)

Experimental results

Experimental results

Tanaphol Thaipanich Thaipanich@wisc.edu Thank you Tanaphol Thaipanich Thaipanich@wisc.edu