Face Detection by BoostingObjectiveTesting Phase - Scheme of Viola-Jones face detectorTraining Phase - Training Dataset FeatureFeature PoolFeature PoolTraining Phase - How to Build the Cascaded DetectorExperimental SetupExperimental ResultsExperimental ResultsExperimental ResultsExperimental ResultsAnalysisReferencesThanks!Face Detection by BoostingLiefei XuApril 30, 2009CS559 Machine Learning Course ProjectObjective Detecting Faces in Given ImagesAlgorithm Viola-Jones face detector Use a set of weak classifiers to create a strong classifier.- Weak classifiere.g. a classifier has correct detection rate 99.9% + false positive rate 40%. - Strong classifiere.g. a classifier has correct detection rate 98% + false positive rate 0.01%Testing Phase- Scheme of Viola-Jones face detectorIf we have 10 layers, at each layer correct detection rate is 99.9%, false positive rate is 40%,the final correct detection rate is 99.9%^10=99%, false positive rate is 40%^10=0.01%Training Phase- Training Dataset  MIT CBCL dataset All faces are cropped and resized to 19*19 Non-face images use the same image size.FeatureA BFeature = I(White area) – I(Black area) The differences between different features are the location and size of the white and black area.Classifier A: if I(White area) – I(Black area) > θ1then sub-window is a faceClassifier B:if I(White area) – I(Black area) > θ2then sub-window is a faceFeature Pool…………ABCDPrototype…EFeature Pool• number of features for a sub-window size of 19*19Feature Type #A 34,200B 34,200C 21,660D 21,660E 16,200Sum 127,920Training Phase- How to Build the Cascaded Detector At each layer, combine several classifier to create a weak classifier Cascade the weak classifiers at each layer to create a strong classifier Stop when the false positive rate decreases to the requirement. Otherwise, add one more layer.Feature a Feature bclassifier1 classifier2layer1Feature a Feature bclassifier1 classifier2layer2Feature c Feature dclassifier3 classifier4Experimental Setup All experiments were run on Dell XPS 730x desktop, CPU Intel® Core™ i7-920 , 6G memory.  Matlab , GML AdaBoost Matlab Toolbox Training dataset - MIT CBCL : ◦ 1stlayer: 1000 faces + 2000 non-faces◦ 2ndlayer: 1000 faces + FP non-faces from 1stlayer◦ 3rdlayer: 1000 faces + FP non-faces from 2ndlayerTesting dataset – MIT CBCL & LFW ◦ 500 faces + 500 non-faces from MIT CBCL testing dataset◦ 1000 faces from Labeled Face in the Wild dataset2 3-layer Detectors◦ Detector 1: 25 – 25 – 63 features◦ Detector 2: 12 – 12 – 20 featuresExperimental Results Training◦ Computing features Computing 127,920 feature values, Over total 3000 (1000 faces + 2000 non-faces) images, Each image is 19*19, costs 33 hours.◦ Selecting featuresLayer # of FeaturesTraining time (in minutes)RecallFalse Positive Rate# of False PositiveDetector 1 Layer 1 25 60.93 99.9% 30.77% 444Layer 2 25 31.62 99.9% 16.19% 192Layer 3 63 58.85 100% 0.00% 0Detector 2 Layer 1 12 33.28 99.2% 48.92% 942Layer 2 12 23.43 99.3% 35.10% 530Layer 3 20 26.83 99% 15.96% 178Layer1 FPLayer2FPLayer3FPExperimental Results Tests on 500 faces+500 non-faces (19*19) from MIT CBCL datasetTesting time(in seconds)Recall FalsePositive RateCombined RecallCombined False Positive RateDetector 1 Layer 1 15 94.8% 11.40% 94.8% 11.40%Layer 2 97.0% 25.27% 92.2% 5.92%Layer 3 95.4% 21.93% 88.0% 3.72%Detector 2 Layer 1 18 97.8% 27.45% 97.8% 27.45%Layer 2 96.8% 29.45% 95.0% 16.08%Layer 3 82.6% 24.50% 79.8% 7.42% Tests on 1000 faces from Labeled Face in the Wild datasetExperimental ResultsScale Testing time (in hour)RecallFalse Positive RateDetector 1 1.1 2.933 83.0% 51.63%1.2 1.624 84.3% 53.50%Detector 2 1.1 2.3 78.4% 63.55%1.2 1.382 80.2% 63.11%Experimental ResultsAnalysis More training data 1000 faces+2000 non-faces vs. 4916 faces+10,000 non-faces Profile image and rotated features Use 24*24 instead of 19*19 faces 24*24 is tested better, includes extra visual information Add more layers 3 layers 113 features vs. 32 layers 4297 features Improve matlab code, decrease detecting timeReferences Paul Viola, Michael Jones, “Robust Real-time Object Detection”, 2ndinternational workshop on statistical and computational theories of vision – modeling, learning, computing, and sampling, 2001 Rainer Lienhart, Alexander Kuranov, Vadim Pisarevsky, “Empirical Analysis of Detecion Cascades of Boosted Classifiers for Rapid Object Detection”, Microprocessor Research Lab, Intel Labs, Technical Report, Dec. 2002 The laboratory of computer graphics at dept. of CS MSU, “GML AdaBoost Matlab Toolbox