Facial Re-identification Using Deep Learning on Combined Real-Virtual Environments

点击翻译按钮获取中文摘要

Face recognition, identification and verification are common problems in the field of biometrics. Many approaches have been developed over the years resulting in robust and accurate methodologies, specially with the arrival of deep neural networks. However, in spite of the ongoing progress, algorithms still suffer when scaling to hundreds/thousands of users with the multiple sources of variability introduced by the environment and the user context, such as head orientation, facial accessories, different backgrounds and the use of different recording devices.The aim of this work is to address these limitations and to develop a novel unified framework for feature extraction and metric learning using deep learning architectures. By assimilating face recognition to re-identification, we aim to extend the application of our methodology to vast datasets such as current social networks or national police databases, where in most cases only a few images per subject are available (for example their profile picture or police mugshots). The algorithm should be able to use these few photos to identify subjects in CCTV footage at low resolution and image quality in different view-points and poses. The testing of such a virtually enhanced face re-identification paradigm on real world surveillance cameras will be the underlying objective of this project. The project will aim to develop an approach which can use one or two images from police mugshots to identify individuals in CCTV stream.The objectives of this work include: - To implement deep convolutional neural networks to tackle automatic feature extraction. - To combine automatic feature extraction with metric learning in Siamese network architectures, to specifically address the problem of verification and re-identification. - To investigate new configurations for face recognition in zero-shot and one-shot scenarios. - To extend the re-identification framework to allow image-to-video recognition that's able to tackle poor quality CCTV footage. - To explore robust strategies such as data augmentation and dropout to address facial occlusions and other sources of variation which may affect identification, such as different poses, clothing, cosmetics, glasses, scarfs, hats, etc. - To develop new deep learning architectures that enhance visual facial re-identification using semantic information from the context, or the subject profile. - To evaluate the performance and limitation of verification versus re-identification, to link real life snapshots with social network profiles and/or CCTV captures.This research has potential impact for crime prevention -where mugshots can be matched against CCTV cameras, human trafficking -where pictures in illegal websites can be compared against images taken at the port of entry- and increasing security in airports, -where passport pictures or images in a suspect watch list are compared against the airport cameras, leading to the implementation of more secure and transparent e-gates. This research fits the EPSRC Research areas of Artificial Intelligence Technologies and Image and Vision Computing.