Hi all, Hope you all are doing all. Today I will explain the face detection procedure used in opencv.
Procedure :
Procedure :
Step1: create cascaded classifier using the training
algorithm provided by opencv and get the .xml file.
Step2: load the pre-made .xml file.
Step3: input frame from camera/ input image and convert it
to grey scale image.
Step4: use opencv’s ‘CascadeClassifier:: detectMultiScale()’
function to detect faces of different sizes in the input image.
Explanation of CascadeClassifier::detectMultiScale() –
Parameters:
i. image - Matrix of the type CV_8U containing an image
where objects are detected.
ii. objects – Vector of rectangles where each rectangle
contains the detected object.
iii. scaleFactor – Parameter specifying how much the image
size is reduced at each image scale.
iv. minNeighbors – Parameter specifying how many neighbour's
each candidate rectangle should have to retain it.
v. minSize – Minimum possible object size. Objects smaller
than that are ignored.
vi. maxSize – Maximum possible object size. Objects larger
than that are ignored.
• Basically what ‘CascadeClassifier::
detectMultiScale()’ does is it takes the original image and creates an image
pyramid from it, using the resize factor and searches for faces/objects in it. Image
pyramid is a multi-scale representation of an image, such that the face
detection can be scale-invariant, i.e., detecting large and small faces using
the same detection window.
• This
gives the ability of detecting faces/objects at a single model scale,
throughout different images scales, meaning that if a detection happens at a
specific layer, the bounding box will be rescaled the same amount as the
original image was to reach that pyramid layer.
• Using
this technique you can detect multiple people scales at only a single model
scale, which is computationally less expensive than training a model for each
possible scale and running those over the single image.
• To make a good detector you need to train the cascade properly with a good number of sample images. Here is an example-
