Covariate Analysis for View-point Independent Gait Recognition

Many studies have shown that gait can be deployed as a biometric. Few of these have addressed the effects of view-point and covariate factors on the recognition process. We describe the first analysis which combines view-point invariance for gait recognition which is based on a model-based pose estimation approach from a single un-calibrated camera. A set of experiments are carried out to explore how such factors including clothing, carrying conditions and view-point can affect the identification process using gait. Based on a covariate-based probe dataset of over 270 samples, a recognition rate of 73.4% is achieved using the KNN classifier. This confirms that people identification using dynamic gait features is still perceivable with better recognition rate even under the different covariate factors. As such, this is an important step in translating research from the laboratory to a surveillance environment

Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras

Despite the fact that personal privacy has become a major concern, surveillance technology is now becoming ubiquitous in modern society. This is mainly due to the increasing number of crimes as well as the essential necessity to provide secure and safer environment. Recent research studies have confirmed now the possibility of recognizing people by the way they walk i.e. gait. The aim of this research study is to investigate the use of gait for people detection as well as identification across different cameras. We present a new approach for people tracking and identification between different non-intersecting un-calibrated stationary cameras based on gait analysis. A vision-based markerless extraction method is being deployed for the derivation of gait kinematics as well as anthropometric measurements in order to produce a gait signature. The novelty of our approach is motivated by the recent research in biometrics and forensic analysis using gait. The experimental results affirmed the robustness of our approach to successfully detect walking people as well as its potency to extract gait features for different camera viewpoints achieving an identity recognition rate of 73.6 % processed for 2270 video sequences. Furthermore, experimental results confirmed the potential of the proposed method for identity tracking in real surveillance systems to recognize walking individuals across different views with an average recognition rate of 92.5 % for cross-camera matching for two different non-overlapping views.<br/

Gait Analysis and Recognition for Automated Visual Surveillance

Human motion analysis has received a great attention from researchers in the last decade due to its potential use in different applications such as automated visual surveillance. This field of research focuses on the perception and recognition of human activities, including people identification. We explore a new approach for walking pedestrian detection in an unconstrained outdoor environment. The proposed algorithm is based on gait motion as the rhythm of the footprint pattern of walking people is considered the stable and characteristic feature for the classification of moving objects. The novelty of our approach is motivated by the latest research for people identification using gait. The experimental results confirmed the robustness of our method to discriminate between single walking subject, groups of people and vehicles with a successful detection rate of 100%. Furthermore, the results revealed the potential of our method to extend visual surveillance systems to recognize walking people. Furthermore, we propose a new approach to extract human joints (vertex positions) using a model-based method. The spatial templates describing the human gait motion are produced via gait analysis performed on data collected from manual labeling. The Elliptic Fourier Descriptors are used to represent the motion models in a parametric form. The heel strike data is exploited to reduce the dimensionality of the parametric models. People walk normal to the viewing plane, as major gait information is available in a sagittal view. The ankle, knee and hip joints are successfully extracted with high accuracy for indoor and outdoor data. In this way, we have established a baseline analysis which can be deployed in recognition, marker-less analysis and other areas. The experimental results confirmed the robustness of the model-based approach to recognise walking subjects with a correct classification rate of 95% using purely the dynamic features derived from the joint motion. Therefore, this confirms the early psychological theories claiming that the discriminative features for motion perception and people recognition are embedded in gait kinematics. Furthermore, to quantify the intrusive nature of gait recognition we explore the effects of the different covariate factors on the performance of gait recognition. The covariate factors include footwear, clothing, carrying conditions and walking speed. As far as the author can determine, this is the first major study of its kind in this field to analyse the covariate factors using a model-based method

Gait analysis and recognition for automated visual surveillance

Human motion analysis has received a great attention from researchers in the last decade due to its potential use in different applications such as automated visual surveillance. This field of research focuses on the perception and recognition of human activities, including people identification. We explore a new approach for walking pedestrian detection in an unconstrained outdoor environment. The proposed algorithm is based on gait motion as the rhythm of the footprint pattern of walking people is considered the stable and characteristic feature for the classification of moving objects. The novelty of our approach is motivated by the latest research for people identification using gait. The experimental results confirmed the robustness of our method to discriminate between single walking subject, groups of people and vehicles with a successful detection rate of 100%. Furthermore, the results revealed the potential of our method to extend visual surveillance systems to recognize walking people. Furthermore, we propose a new approach to extract human joints (vertex positions) using a model-based method. The spatial templates describing the human gait motion are produced via gait analysis performed on data collected from manual labeling. The Elliptic Fourier Descriptors are used to represent the motion models in a parametric form. The heel strike data is exploited to reduce the dimensionality of the parametric models. People walk normal to the viewing plane, as major gait information is available in a sagittal view. The ankle, knee and hip joints are successfully extracted with high accuracy for indoor and outdoor data. In this way, we have established a baseline analysis which can be deployed in recognition, marker-less analysis and other areas. The experimental results confirmed the robustness of the model-based approach to recognise walking subjects with a correct classification rate of 95% using purely the dynamic features derived from the joint motion. Therefore, this confirms the early psychological theories claiming that the discriminative features for motion perception and people recognition are embedded in gait kinematics. Furthermore, to quantify the intrusive nature of gait recognition we explore the effects of the different covariate factors on the performance of gait recognition. The covariate factors include footwear, clothing, carrying conditions and walking speed. As far as the author can determine, this is the first major study of its kind in this field to analyse the covariate factors using a model-based method.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

On Using Gait in Forensic Biometrics

Given the continuing advances in gait biometrics, it appears prudent to investigate the translation of these techniques for forensic use. We address the question as to the confidence that might be given between any two such measurements. We use the locations of ankle, knee and hip to derive a measure of the match between walking subjects in image sequences. The Instantaneous Posture Match algorithm, using Harr templates, kinematics and anthropomorphic knowledge is used to determine their location. This is demonstrated using real CCTV recorded at Gatwick Airport, laboratory images from the multi-view CASIA-B dataset and an example of real scene of crime video. To access the measurement confidence we study the mean intra- and inter-match scores as a function of database size. These measures converge to constant and separate values, indicating that the match measure derived from individual comparisons is considerably smaller than the average match measure from a population

The emergence of nanocellulose aerogels in CO2 adsorption

Mitigating the effect of climate change toward a sustainable development is one of the main challenges of our century. The emission of greenhouse gases, especially carbon dioxide (CO2), is a leading cause of the global warming crisis. To address this issue, various sustainable strategies have been formulated for CO2 capture. Renewable nanocellulose aerogels have risen as a highly attractive candidate for CO2 capture thanks to their porous and surface-tunable nature. Nanocellulose offer distinctive characteristics, including significant aspect ratios, exceptional biodegradability, lightweight nature, and the ability for chemical modification due to the abundant presence of hydroxyl groups. In this review, recent research studies on nanocellulose-based aerogels designed for CO2 absorption have been highlighted. The state-of-the-art of nanocellulose-based aerogel has been thoroughly assessed, including their synthesis, drying methods, and characterization techniques. Additionally, discussions were held about the mechanisms of CO2 adsorption, the effects of the porous structure, surface functionalization, and experimental parameters. Ultimately, this synthesis review provides an overview of the achieved adsorption rates using nanocellulose-based aerogels and outlines potential improvements that could lead to optimal adsorption rates. Overall, this research holds significant promise for tackling the challenges of climate change and contributing to a more sustainable future.The authors would like to acknowledge the financial support of the Basque Government (project IT1498-22) and the University of the Basque Country (PIF21/52)

Acid Hydrolysis of Almond Shells in a Biphasic Reaction System: Obtaining of Purified Hemicellulosic Monosaccharides in a Single Step

The aim of this work is to comprehend the biphasic reaction systems through another perspective; the simultaneous purification and production of carbohydrates during the pretreatment of biomass. A dilute acid hydrolysis of almond shells in a 2-Methyltetrahydrofuran/H2O system was optimised to maximise the obtaining of hemicellulose-derived monosaccharides with the minimum formation of degradation products. The optimised conditions of the biphasic reaction system, which produced 205.3 g hemicellulose-derived monosaccharides/Kg almond shells, were replicated in a monophasic reaction system to assess the benefits of the biphasic reaction systems. The latest system allowed the removal of 85.3% of the furans generated during the dilute acid hydrolysis, creating antioxidant extract, together with the catalysis of the hydrolysis of the hemicelluloses in a 20%. Therefore, the proposed process could become a promising method to purify carbohydrates with an environmentally friendly procedure that allowed the obtaining of multiple added-value products in a single step.Dr. I. Dávila would like acknowledge to the University of the Basque Country (UPV/EHU) for the financial support (Grant reference No. DOCREC19/47

Electrochemical Activity of Lignin Based Composite Membranes

Our society’s most pressing challenges, like high CO2 emission and the constant battle against energy poverty, require a clean and easier solution to store and utilize the renewable energy resources. However, recent electrochemical components are expensive and harmful to the environment, which restricts their widespread deployment. This study proposes an easy method to synthesize and fabricate composite membranes with abundantly found biomass lignin polymer to replace conventional costly and toxic electrode materials. Easier manipulation of lignin within the polymeric matrix could provide the improved composite to enhance its electrochemical activity. Our major focus is to activate the quinone moiety via oxidation in the polymeric mixture using a strong ionic acid. The physico-chemical and electrochemical characterizations of two different lignins within varied polymeric mixture compositions have been carried out to confirm that the redox properties of pure unmodified lignin could be achieved via intrinsic mutual sharing of the structural properties and intercross linkage leading to improved integrity and redox activity/conductivity.This research was funded by the Basque Government (project IT1008-16)

Optimal Taxation and Economic Growth in Tunisia: Short and Long Run Analysis

Tax policy is among the most common and relevant instruments in the toolkit of policy-makers when thinking about promoting growth, yet there is not compelling evidence regarding its effect in Tunisia. Using a variety of approaches, we measure firstly the optimal tax burden rate using Scully's static model and the quadratic model. For Scully's static model, gross domestic product is the dependent variable. For the quadratic model, growth rate is a dependent variable explained by tax rate in level and in square. Secondly and according to stationary and cointegration test results, we focus on the long-term effects on gross domestic product of the important taxes, namely tax revenue and private receipts. In this second study, we use a basic Scully model and we develop a vector error correction model technique. Our results show that optimal tax burden rate has to be situated between 12.8% and 19.6% of gross domestic product which is widely lower than the current rates. The long-term analysis estimates an optimal rate of 14% of gross domestic product which can participate to increase economic growth, to stabilize the tax evasion and to encourage investment especially after the Tunisian revolution

Nanocellulose-based sensing platforms for heavy metal ions detection: A comprehensive review.

Increase in industrial activities has been arising a severe concern about water pollution caused by heavy metal ions (HMIs), such us lead (Pb2+), cadmium (Cd2+) or mercury (Hg2+). The presence of substantial amounts of these ions in the human body is harmful and can cause serious diseases. Hence, the detection of HMIs in water is of great importance. As technological advances have developed, some conventional methods have become obsolete due to some methodological disadvantages, giving way to a second generation that uses novel sensors. Recently, nanocellulose, as a biocompatible material, has drawn a remarkable attraction for developing sensors owing to its extraordinary physical and chemical properties. This review pays a special attention to the different dimensional nanocellulose-based sensors devised for HMIs recognition. What is more, different sensing techniques (optical and electrochemical), sensing mechanisms and the roles of nanocellulose in such sensors are discussed.The authors would like to thank the University of the Basque Country (Training of Researcher Staff, PIF 20/197)