Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network

Disguised face identification (DFI) is an extremely challenging problem due to the numerous variations that can be introduced using different disguises. This paper introduces a deep learning framework to first detect 14 facial key-points which are then utilized to perform disguised face identification. Since the training of deep learning architectures relies on large annotated datasets, two annotated facial key-points datasets are introduced. The effectiveness of the facial keypoint detection framework is presented for each keypoint. The superiority of the key-point detection framework is also demonstrated by a comparison with other deep networks. The effectiveness of classification performance is also demonstrated by comparison with the state-of-the-art face disguise classification methods.Comment: To Appear in the IEEE International Conference on Computer Vision Workshops (ICCVW) 201

The Science of Disguise

Technological advances have made digital cameras ubiquitous, to the point where it is difficult to purchase even a mobile phone without one. Coupled with similar advances in face recognition technology, we are seeing a marked increase in the use of biometrics, such as face recognition, to identify individuals. However, remaining unrecognized in an era of ubiquitous camera surveillance remains desirable to some citizens, notably those concerned with privacy. Since biometrics are an intrinsic part of a person\u27s identity, it may be that the only means of evading detection is through disguise. We have created a comprehensive database of high-quality imagery that will allow us to explore the effectiveness of disguise as an approach to avoiding unwanted recognition. Using this database, we have evaluated the performance of a variety of automated machine-based face recognition algorithms on disguised faces. Our data-driven analysis finds that for the sample population contained in our database: (1) disguise is effective; (2) there are significant performance differences between individuals and demographic groups; and (3) elements including coverage, contrast, and disguise combination are determinative factors in the success or failure of face recognition algorithms on an image. In this dissertation, we examine the present-day uses of face recognition and their interplay with privacy concerns. We sketch the capabilities of a new database of facial imagery, unique both in the diversity of the imaged population, and in the diversity and consistency of disguises applied to each subject. We provide an analysis of disguise performance based on both a highly-rated commercial face recognition system and an open-source algorithm available to the FR community. Finally, we put forth hypothetical models for these results, and provide insights into the types of disguises that are the most effective at defeating facial recognition for various demographic populations. As cameras become more sophisticated and algorithms become more advanced, disguise may become less effective. For security professionals, this is a laudable outcome; privacy advocates will certainly feel differently

Deep Spiking Neural Network for Video-based Disguise Face Recognition Based on Dynamic Facial Movements

With the increasing popularity of social media andsmart devices, the face as one of the key biometrics becomesvital for person identification. Amongst those face recognitionalgorithms, video-based face recognition methods could make useof both temporal and spatial information just as humans do toachieve better classification performance. However, they cannotidentify individuals when certain key facial areas like eyes or noseare disguised by heavy makeup or rubber/digital masks. To thisend, we propose a novel deep spiking neural network architecturein this study. It takes dynamic facial movements, the facial musclechanges induced by speaking or other activities, as the sole input.An event-driven continuous spike-timing dependent plasticitylearning rule with adaptive thresholding is applied to train thesynaptic weights. The experiments on our proposed video-baseddisguise face database (MakeFace DB) demonstrate that theproposed learning method performs very well - it achieves from95% to 100% correct classification rates under various realisticexperimental scenario

Applying psychological science to the CCTV review process: a review of cognitive and ergonomic literature

As CCTV cameras are used more and more often to increase security in communities, police are spending a larger proportion of their resources, including time, in processing CCTV images when investigating crimes that have occurred (Levesley & Martin, 2005; Nichols, 2001). As with all tasks, there are ways to approach this task that will facilitate performance and other approaches that will degrade performance, either by increasing errors or by unnecessarily prolonging the process. A clearer understanding of psychological factors influencing the effectiveness of footage review will facilitate future training in best practice with respect to the review of CCTV footage. The goal of this report is to provide such understanding by reviewing research on footage review, research on related tasks that require similar skills, and experimental laboratory research about the cognitive skills underpinning the task. The report is organised to address five challenges to effectiveness of CCTV review: the effects of the degraded nature of CCTV footage, distractions and interrupts, the length of the task, inappropriate mindset, and variability in people’s abilities and experience. Recommendations for optimising CCTV footage review include (1) doing a cognitive task analysis to increase understanding of the ways in which performance might be limited, (2) exploiting technology advances to maximise the perceptual quality of the footage (3) training people to improve the flexibility of their mindset as they perceive and interpret the images seen, (4) monitoring performance either on an ongoing basis, by using psychophysiological measures of alertness, or periodically, by testing screeners’ ability to find evidence in footage developed for such testing, and (5) evaluating the relevance of possible selection tests to screen effective from ineffective screener

Aging effects in automated face recognition

The main objective of this work was to analyze the effects of aging on the automated face recognition process. A dataset was used to perform experiments and obtain indicators to measure the impact of aging. To compare the effects of aging the dataset was segmented based on the age difference between the subjects’ face images. The image quality metrics were also part of the analysis performed in this study. The results of the experiments shown that the higher the gap between the images, the higher the error rates. These were the expected results and it is consistent with other experiments performed in the past. The False Rejection Rate (FRR) was measured at 1%, 0.1%, and 0.01% False Acceptance Rate (FAR) obtaining the similar output as the gap between the images increased