FACE DETECTION AND TRACKING

Abstract

With the use of various technological advancements and devices in today’s routine life, detection and tracking of human faces and facial features become very essential areas of focus. They become important so that more techniques could be developed for increasing their working efficiency. The field of Computer Vision uses these intermediary processes of face detection and tracking to track and analyze the input of visual information about humans, their faces and/or body movements, and correspondingly proceed to the desired application. The present thesis work has been taken up for the development of (i) an optimiz- able face detection and tracking model based on facial landmark localisation and feature tracking, for better and efficient processing of faces in high quality video streams, and (ii) a Non-Neighbourhood Background Elimination component us- ing the built model with mathematical and statistical modelling, for reducing the processing time and computations required for finding the target face in a frame when it has already been detected. Many algorithms have been developed to facilitate Face Detection and Track- ing applications. Viola and Jones were able to develop an algorithm in 2004, that achieves real-time performance with decent accuracy in detecting faces. It was one of the first algorithms to achieve such efficient performance, that is why it is still used as a standard algorithm to compare against other upcoming algorithms, and therefore has been specifically discussed in this thesis. vii There is a lot of visual information that is generated in various fields, rang- ing from daily routine to specialized applications. Processing all these types of information efficiently is a valid concern and need focused research. Most face detection algorithms have to deal with low quality data in videos, since they’re mainly focused on surveillance applications, whose information capturing devices capture less information per frame. Consequently, this thesis reviews some state- of-the-art face detection algorithms and compares their processing efficiency on low and high quality videos. The comparative analysis reveals that these recent and modern algorithms do not work as effectively on high quality videos as they do on lower quality videos. Therefore, there is an increasing need to focus re- search on analysis of high quality information in videos in an efficient manner, so as to keep up the pace of their analysis with the information that is generated. High quality videos (data generated by current applications like social media, Multimedia content, etc) mostly exist in offline mode, that could be used for post processing by the Computer Vision applications. To address this need, an effort has been made to focus on developing such an algorithm that gives faster results on high quality videos, at par with the algorithms working on live low quality video feeds. The proposed algorithm uses Convolutional-MTCNN as base algo- rithm, and speeds it up for high definition videos. The proposed model speeds up the face detection process really fast, up to 19+ FPS, while still maintaining above 90% accuracy. This paper also presents a novel solution to the problem of occlusion and detecting partial or fully hidden faces in the videos. This is achieved by using statistical and probabilistic approaches, given that the face has been identified in first few frames, to give the algorithm an estimate of where the face should be in the occluded region. Since the focus of our research is to efficiently process high quality data, some viii commercially used face detection algorithms in open literature have also been considered in our research. Models like FaceNet, HOG, YuNet, alongwith Viola- Jones algorithm and MTCNN, have been discussed and analysed in our thesis. The research done is compared against these models, in an effort to improve their performance in commercial settings. Further analysis lead to the conclusion that modern face detection algorithms fail to provide optimal results when they have to deal with larger amounts of data per frame while processing higher quality videos. This thesis discusses an- other proposed work that tackles discussed problem and offers a solution to deploy commercially used state-of-the-art face detection algorithms to process only the regions of interest in a frame, and discard the rest to decrease the data to be processed. The model maintains the accuracy of the base algorithm while de- creasing the processing time per frame, thereby increasing the overall efficiency. The selection of region of interest is dependent on the detection of facial window in the previous frame. Therefore, the choice of base algorithm plays an important role in determining the speed of the model. The model achieves increased pro- cessing speeds of about 69–76% more than the standalone usage of the detection algorithms for analyzed frame rates.