Face Recognition and Classification Using GoogleNET Architecture

Abstract

Face recognition is the most important tool in computer vision and an inevitable technology finding applications in robotics, security, and mobile devices. Though it is a technology of the past, state-of-the-art machine learning (ML) techniques have made this technology game-changing and even surpass human counterparts in terms of accuracy. This paper focuses on applying one of the advanced machine learning tools in face recognition to achieve higher accuracy. We created our own dataset and trained it on the GoogleNet (inception) deep learning model using the Caffe and Nvidia DIGITS framework. We achieved an overall accuracy of 91.43% which was fairly high enough to recognize the faces better than the conventional ML techniques. The scope of the application of deep learning is enormous and by training a huge volume of data with massive computational power, accuracy greater than 99% can be achieved. This paper will give a glimpse of deep learning, from creation of dataset to training and deploying the models, and the method can be applied for dataset corresponding to any field, be it medicine, agriculture or manufacturing, reducing the human effort and thus triggering the revolution of automation.