Rapid Object Detection Using a Boosted Cascade of Simple Features

INTRODUCTION

Computer vision is a diverse and relatively new field of study. In the early

days of computing, it was difficult to process even moderately large sets of image

data. It was not until the late 1970s that a more focused study of the field emerged.

Computer vision covers a wide range of topics which are often related to other

disciplines, and consequently there is no standard formulation of "the computer

vision problem". Moreover, there is no standard formulation of how computer

vision problems should be solved. Instead, there exists an abundance of methods

for solving various well-defined computer vision tasks, where the methods often

are very task specific and seldom can be generalised over a wide range of

applications. Many of the methods and applications are still in the state of basic

research, but more and more methods have found their way into commercial

products, where they often constitute a part of a larger system which can solve

complex tasks (e.g., in the area of medical images, or quality control and

measurements in industrial processes). In most practical computer vision

applications, the computers are pre-programmed to solve a particular task, but

methods based on learning are now becoming increasingly common.

The classical problem in computer vision, image processing, and machine

vision is that of determining whether or not the image data contains some specific

object, feature, or activity. This task can normally be solved robustly and without

effort by a human, but is still not satisfactorily solved in computer vision for the

general case: arbitrary objects in arbitrary situations. The existing methods for

dealing with this problem can at best solve it only for specific objects, such as

simple geometric objects (e.g., polyhedra), human faces, printed or hand-written

characters, or vehicles, and in specific situations, typically described in terms of

well-defined illumination, background, and pose of the object relative to the

camera.

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Face detection can be regarded as a specific case of object-class detection. In

object-class detection, the task is to find the locations and sizes of all objects in an

image that belong to a given class. Examples include upper torsos, pedestrians, and

cars.

Face detection can be regarded as a more general case of face localization. In

face localization, the task is to find the locations and sizes of a known number of

faces (usually one). In face detection, one does not have this additional

information.

Early face-detection algorithms focused on the detection of frontal human

faces, whereas newer algorithms attempt to solve the more general and difficult

problem of multi-view face detection. That is, the detection of faces that are either

rotated along the axis from the face to the observer (in-plane rotation), or rotated

along the vertical or left-right axis (out-of-plane rotation), or both. The newer

algorithms take into account variations in the image or video by factors such as

face appearance, lighting, and pose. The most popular algorithms are:

- Viola-Jones object detection framework - Viola & Jones

- Schneiderman & Kanade

- Rowley, Baluja & Kanade: Neural Network-based Face Detection

While detection rates are nearly the same, the speed of the Viola-Jones is roughly

15 times faster than the Rowley-Baluja-Kanade detector [12] and about 600 times

faster than the Schneiderman-Kanade detector [15].