ILLUMINATION INVARIENT FACE RECOGNITION USING DEEP LEARNING

Abstract

Varying illumination conditions have always been bottleneck in recognizing faces under wild environment. Because changing the lightning conditions and position alter the shadows, brightness and shadows which result in the form of changing facial features, which as a whole turn the facial geometry and therefore made it difficult for a system to detect a face under such variations. Our goal here was to develop a system which can recognize face, unaffected of the illumination invariance and Convolutional Neural Networks allow us to extract a wide range of features from images. Turns out, we can use this idea of feature extraction foi face lecognition too under wild illumination conditions. can i The approach we are going to use for face recognition is fairly straight forward. The key here is to get a deep neural network to produce a bunch of numbeis that desciibe a face (known as face encodings). When you pass different images of the same person under different, illumination impact, the network should return similar outputs (i.e. closer numbeis) foi both images, whereas when you pass in the images of two different people, the nelwoik should letuin very different outputs for the two images. This means that the neural network needs to be trained to automatically identify different features of faces and calculate numbers based identifier for a particular person’s face — if I on that. The output of the neural network can be thought of as you pass in different images of the same person, the output of the neural network will be very similar/close, whereas if you pass in images of a different person, the output will be very different and all this process is running under varying illumination conditions and proves that control these variations and give fruitful results independent of the an ; CNNs are good enough to way in which lightning is to be used.