Introduction Face detection is a computer technology that determines the locations and sizes of human faces in arbitrary (digital) images. It detects facial features and ignores anything else, such as buildings, trees and bodies. Face detection can be regarded as a more general case of face localization. The first step in automatic facial recognition is the accurate detection of human faces in an arbitrary scene. When faces are localized exactly, the recognition is performed on the detected face. Haar Cascade Detection It is a machine learning approach where a cascade function is trained from a lot of positive and negative images. This method was proposed by Paul Viola and Michael Jones in their Paper "Rapid Object Detection using a Boosted Cascade of Simple Features" in 2001. Initially, the algorithm needs a lot of positive (Images of Faces) and negative Images (images without faces) to train the classifier. Then we need to extract Haar features fro
(a)Final Blended Image (b) Background Image (c)Foreground Image Alpha blending Alpha blending is the process of overlaying a foreground image with transparency over a background Image. The transparent image is generally a PNG image.It consists of four channels (RGBA).The fourth channel is the alpha channel which holds the transparency magnitude. Image (b) is a background image and image (c) is the foreground / overlay image. Image (a) is the final blended image obtained by blending the overalay image using the alpha mask. Below is the image(Fig d) of the alpha channel of the overlay image. (d).Alpha Channel At every pixel of the image, we blend the background and foreground image color(F) and background color (B) using the alpha mask . At every pixel value of alpha lie in range(0,255), a pixel intensity of 0 means black color and pixel instensity of 255 means whit