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 can 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 for face recognition too under wild illumination conditions. i! ii 11 ii : 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 numbers that describe 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 numbers) foi both images, whereas when you pass in the images of two different people, the netwoik should return ' ; very different outputs for the two images. This means that the neuial netwoik needs to be tiained on that. The ; to automatically identify different features of faces and calculate numbeis based output of the neural network can be thought of as an identifier for a paiticular peison s face — if 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 : ; CNNs are good enough to way in which lightning is to be used.