The effect of time on gait recognition performance

Many studies have shown that it is possible to recognize people by the way they walk. However, there are a number of covariate factors that affect recognition performance. The time between capturing the gallery and the probe has been reported to affect recognition the most. To date, no study has shown the isolated effect of time, irrespective of other covariates. Here we present the first principled study that examines the effect of elapsed time on gait recognition. Using empirical evidence we show for the first time that elapsed time does not affect recognition significantly in the short to medium term. By controlling the clothing worn by the subjects and the environment, a Correct Classification Rate (CCR) of 95% has been achieved over 9 months, on a dataset of 2280 gait samples. Our results show that gait can be used as a reliable biometric over time and at a distance. We have created a new multimodal temporal database to enable the research community to investigate various gait and face covariates. We have also investigated the effect of different type of clothes, variations in speed and footwear on the recognition performance. We have demonstrated that clothing drastically affects performance regardless of elapsed time and significantly more than any of the other covariates that we have considered here. The research then suggests a move towards developing appearance invariant recognition algorithms. Thi

On including quality in applied automatic gait recognition

Many gait recognition approaches use silhouette data. Imperfections in silhouette extraction have a negative effect on the performance of a gait recognition system. In this paper we extend quality metrics for gait recognition and evaluate new ways of using quality to improve a recognition system. We demonstrate use of quality to improve silhouette data and select gait cycles of best quality. The potential of the new approaches has been demonstrated experimentally on a challenging dataset, showing how recognition capability can be dramatically improved. Our practical study also shows that acquiring samples of adequate quality in arbitrary environments is difficult and that including quality analysis can improve performance markedly

Gait Recognition: Databases, Representations, and Applications

There has been considerable progress in automatic recognition of people by the way they walk since its inception almost 20 years ago: there is now a plethora of technique and data which continue to show that a person’s walking is indeed unique. Gait recognition is a behavioural biometric which is available even at a distance from a camera when other biometrics may be occluded, obscured or suffering from insufficient image resolution (e.g. a blurred face image or a face image occluded by mask). Since gait recognition does not require subject cooperation due to its non-invasive capturing process, it is expected to be applied for criminal investigation from CCTV footages in public and private spaces. This article introduces current progress, a research background, and basic approaches for gait recognition in the first three sections, and two important aspects of gait recognition, the gait databases and gait feature representations are described in the following sections.Publicly available gait databases are essential for benchmarking individual approaches, and such databases should contain a sufficient number of subjects as well as covariate factors to realize statistically reliable performance evaluation and also robust gait recognition. Gait recognition researchers have therefore built such useful gait databases which incorporate subject diversities and/or rich covariate factors.Gait feature representation is also an important aspect for effective and efficient gait recognition. We describe the two main approaches to representation: model-free (appearance-based) approaches and model-based approaches. In particular, silhouette-based model-free approaches predominate in recent studies and many have been proposed and are described in detail.Performance evaluation results of such recent gait feature representations on two of the publicly available gait databases are reported: USF Human ID with rich covariate factors such as views, surface, bag, shoes, time elapse; and OU-ISIR LP with more than 4,000 subjects. Since gait recognition is suitable for criminal investigation applications of the gait recognition to forensics are addressed with real criminal cases in the application section. Finally, several open problems of the gait recognition are discussed to show future research avenues of the gait recognition

It’s the Stability, Stupid! How the Quest to Restore Order After the Soviet Collapse Shaped Russian Popular Opinion

The article of record as published may be found at https://doi.org/10.5129/001041518822704926This article argues that despite the chaos and uncertainty of the post-Soviet period, Russian political outlooks were highly coherent because they were driven by a near consensual desire to achieve greater stability. Based on over-time and cross-section dimensional analyses of a unique dataset of 418 surveys, covering the 1993–2011 period, I show that the popular obsession with restoring order facilitated the consolidation of authoritarianism in Russia. In particular, stability-centric outlooks structured political competition in ways that favored strong-armed incumbent behavior and fostered divisions and extremism among the opposition. These dynamics allowed Russia's increasingly authoritarian regime to rule with minimal use of coercion and largely through the ballot box

Why would Putin invade Ukraine?

The article of record as published may be found at https://www.washingtonpost.com/politics/2022/01/16/why-would-putin-invade-ukraine/The looming threat of a full-scale Russian attack on Ukraine kept the world on edge for much of 2021, and for good reason — an attack of this magnitude would arguably be the most significant invasion of a European country by a more powerful neighbor since Adolf Hitler’s assault on Poland in 1939. But what purpose would this move serve? As Russia’s preparations and threatening rhetoric have mounted, analysts have pointed out that another invasion of Ukraine would make little sense from a foreign and security policy standpoint

Extending quality and covariate analyses for gait biometrics

Recognising humans by the way they walk has attracted a significant interest in recent years due to its potential use in a number of applications such as automated visual surveillance. Technologies utilising gait biometrics have the potential to provide safer society and improve quality of life. However, automated gait recognition is a very challenging research problem and some fundamental issues remain unsolved.At the moment, gait recognition performs well only when samples acquired in similar conditions are matched. An operational automated gait recognition system does not yet exist. The primary aim of the research presented in this thesis is to understand the main challenges associated with deployment of gait recognition and to propose novel solutions to some of the most fundamental issues. There has been lack of understanding of the effect of some subject dependent covariates on gait recognition performance. We have proposed a novel dataset that allows analyses of various covariates in a principled manner. The results of the database evaluation revealed that elapsed time does not affect recognition in the short to medium term, contrary to what other studies have concluded. The analyses show how other factors related to the subject affect recognition performance.Only few gait recognition approaches have been validated in real world conditions. We have collected a new dataset at two realistic locations. Using the database we have shown that there are many environment related factors that can affect performance. The quality of silhouettes has been identified as one of the most important issues for translating gait recognition research to the ‘real-world’. The existing quality algorithms proved insufficient and therefore we extended quality metrics and proposed new ways of improving signature quality and therefore performance. A new fully working automated system has been implemented.Experiments using the system in ‘real-world’ conditions have revealed additional challenges not present when analysing datasets of fixed size. In conclusion, the research has investigated many of the factors that affect current gait recognition algorithms and has presented novel approaches of dealing with some of the most important issues related to translating gait recognition to real-world environments

Gait recognition based on shape and motion analysis of silhouette contours

This paper presents a three-phase gait recognition method that analyses the spatio-temporal shape and dynamic motion (STS-DM) characteristics of a human subject’s silhouettes to identify the subject in the presence of most of the challenging factors that affect existing gait recognition systems. In phase 1, phase-weighted magnitude spectra of the Fourier descriptor of the silhouette contours at ten phases of a gait period are used to analyse the spatio-temporal changes of the subject’s shape. A component-based Fourier descriptor based on anatomical studies of human body is used to achieve robustness against shape variations caused by all common types of small carrying conditions with folded hands, at the subject’s back and in upright position. In phase 2, a full-body shape and motion analysis is performed by fitting ellipses to contour segments of ten phases of a gait period and using a histogram matching with Bhattacharyya distance of parameters of the ellipses as dissimilarity scores. In phase 3, dynamic time warping is used to analyse the angular rotation pattern of the subject’s leading knee with a consideration of arm-swing over a gait period to achieve identification that is invariant to walking speed, limited clothing variations, hair style changes and shadows under feet. The match scores generated in the three phases are fused using weight-based score-level fusion for robust identification in the presence of missing and distorted frames, and occlusion in the scene. Experimental analyses on various publicly available data sets show that STS-DM outperforms several state-of-the-art gait recognition methods

Russia’s Strategic Recalibration After the Ukraine Conflict: Implications for the Two Near-Peer Competitors Strategic Environment (Part 1)

Prepared for: OPNAV/N514. This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). NRP Project ID: NPS-23-N068-AThis research examines the evolving strategic partnership between Russia and China as near-peer, nuclear-armed adversaries for the U.S. in the wake of the war in Ukraine. It focuses on Russia’s new security situation after the conflict in Ukraine, particularly how it might attempt to compensate for its depleted strategic capabilities and resources by partnering with China. The research is organized along two parallel tracks. The first track, contained in this part of the technical report, analyzes how Russia’s nuclear posture has evolved as result of the War in Ukraine, and assesses the impact of the Sino-Russian cooperation on Russia’s space program. The second track of this research, contained in Part 2 of the technical report, examines the cooperation between Russia and China in the nuclear realm, as well as its implications. This research was conducted through a combined team effort of subject-matter experts on Russian strategic doctrines, capabilities, and behavior. The researchers performed a rigorous analysis of the debates in the Russian literature, complementing and contextualizing this information through discussions with subject-matter experts in Washington, the U.S. Strategic Command, and the U.S. Space Command.Approved for public release; distribution is unlimited.Naval Postgraduate School, Naval Research Program (PE 0605853N/2098)Naval Postgraduate School, Naval Research Program OPNAV/N51