Statistical Learning of Multi-View Face Detection Microsoft Research Asia Stan Li, Long Zhu, Zhen Qiu Zhang, Andrew Blake, Hong Jiang Zhang, Harry Shum Presented by Derek Hoiem

Overview Viola-Jones AdaBoost FloatBoost Approach Multi-View Face Detection FloatBoost Results FloatBoost vs. AdaBoost FloatBoost Discussion

Face Detection Overview Evaluate windows at all locations in many scales Classifier Non-Object Object

Viola-Jones AdaBoost Weak classifiers formed out of simple features In sequential stages, features are selected and weak classifiers trained with emphasis on misclassified examples Integral images and a cascaded classifier allow real-time face detection

Viola-Jones Features For a 24 x 24 image: 190,800 semi-continuous features Computed in constant time using integral image Weak classifiers consist of filter response threshold Vertical Horizontal On-Off-On Diagonal

Integral Image y = I8 – I7– I6 + I5+ I4 – I3 – I2 + I1 I( x1, y1 )

Cascade of Classifiers Input Signal (Image Window) 40% Stage 1 1 Weak Classifier 60% 40% Stage 2 5 Weak Classifiers Class 2 (Non-Face) 60% 99.999% … 0.001% 40% Stage N 1200 Weak Classifiers Class 1 (Face)

Viola-Jones AdaBoost Algorithm Strong classifier formed from weak classifiers: At each stage, new weak classifier chosen to minimize bound on classification error (confidence weighted): This gives the form for our weak classifier:

Viola-Jones AdaBoost Algorithm

Viola-Jones AdaBoost Pros and Cons Very fast Moderately high accuracy Simple implementation/concept Greedy search through feature space Highly constrained features Very high training time

FloatBoost Weak classifiers formed out of simple features In each stage, the weak classifier that reduces error most is added In each stage, if any previously added classifier contributes to error reduction less than the latest addition, this classifier is removed Result is a smaller feature set with same classification accuracy

MS FloatBoost Features For a 20 x 20 image: over 290,000 features (~500K ?) Computed in constant time using integral image Weak classifiers consist of filter response threshold Microsoft Viola-Jones

FloatBoost Algorithm

FloatBoost Weak Classifiers Can be portrayed as density estimation on single variables using average shifted histograms with weighted examples Each weak classifier is a 2-bin histogram from weighted examples Weights serve to eliminate overcounting due to dependent variables Strong classifier is a combination of estimated weighted PDFs for selected features

Multi-View Face Detection Head Rotations In-Plane Rotations: -45 to 45 degrees Out of Plane Rotation: -90 to 90 degrees Moderate Nodding

Multi-View Face Detection Detector Pyramid

Multi-View Face Detection Merging Results Frontal Right Side Left Side

Multi-View Face Detection Summary Simple, rectangular features used FloatBoost selects and trains weak classifiers A cascade of strong classifiers makes up the overall detector A coarse-to-fine evaluation is used to efficiently find a broad range of out-of-plane rotated faces

Results: Frontal (MIT+CMU) FloatBoost/AdaBoost/RBK 20x20 images 3000 original faces, 6000 total 100,000 non-faces Schneiderman FloatBoost FloatBoost vs. Adaboost

Results: MS Adaboost vs. Viola-Jones Adaboost More flexible features Confidence-weighted AdaBoost Smaller image size

Results: Profile No Quantitative Results!!!

FloatBoost vs. AdaBoost FloatBoost finds a more potent set of weak classifiers through a less greedy search FloatBoost results in a faster, more accurate classifier FloatBoost requires longer training times (5 times longer)

FloatBoost vs. AdaBoost 1 Strong Classifier, 4000 objects, 4000 non-objects, 99.5% fixed detection

FloatBoost: Pros Very Fast Detection (5 fps multi-view) Fairly High Accuracy Simple Implementation

FloatBoost: Cons Very long training time Not highest accuracy Does it work well for non-frontal faces and other objects?