An Introduction to Face Detection

An Introduction to
Face Detection
Presented By
Dinesh KS Livares Technologies Pvt LtdTech&Socio-Cultural Group

Tech&Socio-Cultural Group
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Livares Technologies Pvt Ltd
Introduction
Object detection is a computer technology related to computer vision and image processing that
deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or
cars) in digital images and videos
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Applications of Object Detection
● Tracking objects
● Medical imaging
● Ball tracking in sports
● Self driving cars
● Face detection and face recognition
● Object extraction from an image or video

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Face Detection
Face detection is a computer technology being used in a variety of applications that identifies
human faces in digital images.
Applications of Object Detection
● Facial motion capture
● Facial recognition
● Photography
● Marketing
● Lip Reading

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Viola & Jones Algorithm
● The Viola- Jones object detection framework is the first object detection
framework to provide competitive object detection rates in real-time
proposed in 2001 by paul Viola and Michael Jones
● Although it can be trained to detect a variety of object classes, it was
motivated primarily by the problem of face detection

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● The characteristics of Viola–Jones algorithm which make it a good
detection algorithm are:
○ Robust – very high detection rate (true-positive rate) & very low false-
positive rate always.
○ Real time – For practical applications at least 2 frames per second must
be processed.
○ Face detection only (not recognition) - The goal is to distinguish faces
from non-faces (detection is the first step in the recognition process).

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The algorithm has four stages:
1. Haar Feature Selection
2. Creating an Integral Image
3. Adaboost Training
4. Cascading Classifiers

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Pixel
1. Each of the pixels that represents an image stored inside a computer has a
pixel value which describes how bright that pixel is, and/or what color it
should be.
2. In the simplest case of binary images, the pixel value is a 1-bit number
indicating either foreground or background.
1. For a grayscale images, the pixel value is a single number that represents
the brightness of the pixel

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Haar Feature Selection
Output image(right) has high intensity at pixels where the convolution kernel
pixel pattern matches perfectly with the input image.

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● Haar-like features are similar to these convolution kernels which are used
to detect the presence of that feature in the given image.
● Each feature results in a single value which is calculated by subtracting the
sum of pixels under white rectangle from the sum of pixels under black
rectangle.
● Value = Σ (pixels in black area) - Σ (pixels in white area)

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Integral Image
● In an integral image the value at pixel (x,y) is the sum of pixels above and
to the left of (x,y)

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Integral Image

There can be approximately 160,000+ features values within a detector at
24x24 base resolution which need to be calculated. But it is to be
understood that only some set of features will be useful among all these
features to identify a face.
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Adaboost

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Adaboost
● Adaboost is a machine learning algorithm which helps in finding only the best
features among all these 160,000+ features. After these features are found a
weighted combination of all these features in used in evaluating and deciding
any given window has a face or not.
● These features are also called as weak classifiers. Adaboost constructs a strong
classifier as a linear combination of these weak classifiers.
Weak Classifier 1
Strong Classifier 1
Weak Classifier 2 Weak Classifier 3

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Cascading
● The basic principle of the Viola-Jones face detection algorithm is to scan the
detector many times through the same image – each time with a new size.
● Even if an image should contain one or more faces it is obvious that an
excessive large amount of the evaluated sub-windows would still be negatives
(non-faces).
● So the algorithm should concentrate on discarding non-faces quickly and
spend more time on probable face regions.
● Hence a single strong classifier formed out of linear combination of all best
features is not a good to evaluate on each window because of computation
cost.

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Cascading
● Therefore a cascade classifier is used which is composed of stages each
containing a strong classifier. So all the features are grouped into several stages
where each stage has certain number of features.
● The job of each stage is used to determine whether a given sub window is
definitely not a face or may be a face. A given sub window is immediately
discarded as not a face if it fails in any of the stage.

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Limitation of Viola Jones Algorithm
Although, there are no major downsides of this algorithm that is why it is still
very popular for recognizing faces in real-time applications.
Some minor limitations of this framework is its inability to detect faces in
certain scenarios like-
● If the face of a person is covered with a mask or something else, then
Viola-Jones might not work perfectly in that case.
● If the faces are not oriented properly, then there is possibility that this
algorithm won't be able to detect those faces.

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Limitation of Viola Jones Algorithm
So, due to these drawbacks it led to the evolution of other algorithms and
state of the art approaches like
● Region-based Convolutional Network (R-CNN),
● You Only Look Once (YOLO),
● Single Shot Detector (SSD) and many more!

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Conclusion
We looked at what face detection is and its applications in the industry.
Then, we went through each of the component of Viola-Jones face detection
framework.
If you are interested in trying it out for yourself, OpenCv has an
implementation OpenCV comes with a trainer as well as detector. If you
want to train your own classifier for any object like car, planes etc. you can
use OpenCV to create one.OpenCV already contains many pre-trained
classifiers for face, eyes, smile etc. Those XML files are stored in
opencv/data/haarcascades/ folder

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