Rapid Object Detection using a Boosted Cascade of Simple Features

Paul Viola, Michael JonesConference on Computer Vision and

Pattern Recognition 2001 ( CVPR 2001 )

Outline• Introduction• Features• Learning Classification Functions• The Attentional Cascade• Result

Introduction

Three Contribution• New image representation - Integral image• Method for constructing a classifier - Selecting a small number of important features using AdaBoost• Method for combining classifiers - In a cascade structure

Features

Three Kind of Features

• Two-rectangle

• Three-rectangle

• Four-rectangle

• Feature value = sum of pixel value in white area - sum of pixel value in black area

Integral Image• Integral Image

Rectangular Sum

Rectangular Sum Location

A 1

B 2-1

C 3-1

D 4+1-(2+3)

Learning Classification

Function

Learning Classification

Function• Very small number of features can form an

effective classifier• Select best classifier feature• Weak classifier

AdaBoost algorithm

AdaBoost algorithm

Learning Result• A frontal face classifier - 200 features (among 180,000) - Detection rate: 95% - False positive rate: 1/14084 - 0.7s to scan an 384*288 pixel image

• First feature selected - The eyes is often darker than the nose and cheeks• Second feature selected - The eyes are darker than the bridge of the nose

The Attentional Cascade

Cascade

Training a cascade of classifiers

• Tradeoffso Features↑ ↔ detection rates ↑o Features↑ ↔ computational time ↓

• Constructing stageso Training classifiers using AdaBoosto Adjust the threshold to minimize false negative

Result

Result• Face training set

o 4916 faces imageo 24*24 pixelso 9544 image o 350 million sub-windows

• The complete face detection cascade haso 38 stageso 6061 featureso 15 times faster than current system

Performance

Performance

Result

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