Face detection is an age old process and is widely used in popular applications like Face ID by Apple, FaceApp, in surveillance cameras in China and many other applications. Today, we have sophisticated machine learning techniques which can perform face detection with near human precision and near real-time performace. What is interesting is that even in 2001, we had algorithms in use for Face detection. Yes, we are going back 2 decades. A time machine learning still was yet to blow up and Face detection's future was uncertain.
Haar-like features are digital image features used in object recognition. They owe their name to their intuitive similarity with Haar wavelets and were used in the first real-time face detector. Historically, working with only image intensities (i.e., the RGB pixel values at each and every pixel of image) made the task of feature calculation computationally expensive. A publication by Papageorgiou et al. discussed working with an alternate feature set based on Haar wavelets instead of the usual image intensities. Viola and Jones adapted the idea of using Haar wavelets and developed the so-called Haar-like features.
These words send a shiver down my spine. But then again, they are the only comfort I get when I use Snapchat these days. "Why is Snapchat scaring this moron?" I don't know about you, BUT I SURE AS HELL don't enjoy sharing my bed with Casper or any other creepy ghosts that this otherworld-R.S.V.P-app has brought to my life. You see, every once in a while I'm doing my dog filter faces like a normal human being in 2017; but then… my cat stops moving and stares at the end of the room… the camera refocuses… and then: it finds an invisible Dalmatian filter standing by my side. I've moved twice already… but NO LONGER!