Real Time Face Recognition using KNN

Abstract

Face Recognition is becoming a new trend in the security authentication systems. Modern FR systems can even detect, if the person is real (live) or not while doing face recognition, preventing the systems being hacked by showing the picture of a real person. I am sure, everyone wondered when Facebook implemented the auto-tagging technique. It identifies the person and tag him/her whenever you upload a picture. It is so efficient that, even when the person’s face is occluded or the picture is taken in darkness, it tags accurately. All these successful face recognition systems are the results of recent advancements in the field of computer vision, which is backed by powerful deep learning algorithms. Let us explore one of such algorithms and see how we can implement a real time face recognition system using KNN algorithm. Face recognition can be done in two ways. Imagine you are building a face recognition system for an enterprise. One way of doing this is by training a neural network model (preferably ConvNetmodel), which can classify faces accurately. As you know for a classifier to be trained well, it needs millions of input data. Collecting that many images of employees, is not feasible. So this method seldom works. The best way of solving this problem is by opting one-shot learning technique. One-shot learning aims to learn information about object categories from one, or only a few, training images. The model still needs to be trained on millions of data, but the dataset can be any, but of the same domain. The pre-trained model that we are going to use in Haarcascade with opencv.