Deep face detection for interactive avatar

Abstract

Face detection is one of the most studied problems of the computer vision field. It is usually the first step for other face related technologies such as face recognition and verification. In this thesis, a face detection system is implemented for an interactive avatar that aims to interact naturally with humans. The implemented system consists convolutional neural networks (CNN) and machine learning which are often used to solve image classification tasks. In general, those have enabled the development of face detection systems to be able to detect faces from real world scenes where the distances and image conditions might vary greatly. The used networks in this thesis are Single Shot Multibox Detector (SSD) and MobileNet. The structure of this thesis can be divided into two broad categories which are theory and implementation. Theory part provides the basic background theories and explanations related to machine learning, neural networks and object detection networks. The goal of the theory part is to introduce all of the main elements that are finally constructing the object detection networks. The implementation part describes how the networks are implemented for a functional system that can detect faces and provide coordinates from those detections. Evaluation part is also related to the implementation since the evaluation aims to measure the performance of the implementation. The purpose of this thesis is to use pre-trained networks and models which are then adapted to meet the requirements of this implementation. The research of different kinds of face detection systems shows that there are multiple different approaches and systems, that are able to detect faces from images. The used networks and structures, as described above, were chosen because of their accuracy, efficiency and modern technologies. The results and performance of the proposed implementation satisfied well the requirements and goals of the system.