16 research outputs found

    A GENDER RECOGNITION EXPERIMENT ON THE CASIA GAIT DATABASE DEALING WITH ITS IMBALANCED NATURE

    Get PDF
    Abstract: The CASIA Gait Database is one of the most used benchmarks for gait analysis among the few non-smallsize datasets available. It is composed of gait sequences of 124 subjects, which are unequally distributed, comprising 31 women and 93 men. This imbalanced situation could correspond to some real contexts where men are in the majority, for example, a sports stadium or a factory. Learning from imbalanced scenarios usually requires suitable methodologies and performance metrics capable of managing and explaining biased results. Nevertheless, most of the reported experiments using the CASIA Gait Database in gender recognition tasks limit their analysis to global results obtained from reduced subsets, thus avoiding having to deal with the original setting. This paper uses a methodology to gain an insight into the discriminative capacity of the whole CASIA Gait Database for gender recognition under its imbalanced condition. The classification results are expected to be more reliable than those reported in previous papers

    Gait-based Gender Classification Considering Resampling and Feature Selection

    Get PDF
    Two intrinsic data characteristics that arise in many domains are the class imbalance and the high dimensionality, which pose new challenges that should be addressed. When using gait for gender classification, benchmarking public databases and renowned gait representations lead to these two problems, but they have not been jointly studied in depth. This paper is a preliminary study that pursues to investigate the benefits of using several techniques to tackle the aforementioned problems either singly or in combination, and also to evaluate the order of application that leads to the best classification performance. Experimental results show the importance of jointly managing both problems for gait-based gender classification. In particular, it seems that the best strategy consists of applying resampling followed by feature selection

    Gaıt-Based Gender Classıfıcatıon Usıng Neutral And Non-Neutral Gaıt Sequences

    Get PDF
    Nötr veya Nötr Olmayan Ardaşık Yürüyüş Tarzlarından Davranış-bağımlı Cinsiyet Klasifikasyonu Biometrik sistem bireyle özleĢik en çok göze çarpan bir özellik veya niteliğe dayalı bir vasıf kullanılarak bireyin tanımlanmasını sağlar. Biometric tanımlayıcılar genellikle davranıĢsal özelliklere karĢı fizyolojik özellikler olarak kategorize edilir. Fizyolojik özellikler Ģahsın parmak izi, avuç içi damarlar, yüz tanıma gibi vücudun yapısal özellikleriyle ilgili olmasına karĢın, Ģahsın davranıĢsal özellikleri yürüme tarzı, imzası ve sesiyle ilgili vasıflarıdır. YürüyüĢ tarzı biometrik tanımlama yöntemi ile kiĢilerin erkek veya kadın olduğunun tanımlamasında kullanılacağı gibi kiĢilerin yürüyüĢ tarzları, yetkisiz kiĢilerin ve cinsiyetlerin belirlenmesi, ve yürüme veya yürümeye bağlı anormalliklerin tespiti gibi farklı uygulama alanlarında kullanılabilir. Bu tezde, kiĢilerin yürüyüĢ özelliklerine göre cinsiyet sınıflandırması yapan bir yöntem önerilmiĢtir. Nötr yürüyüĢ dizilerinin yanı sıra palto/manto giyme (CW) ve çanta taĢıma (CB) gibi nötr olmayan yürüyüĢ tarzlarından kaynaklanan tanımlama sorunları araĢtırılmıĢtır. Cinsiyet sınıflandırma amacıyla farklı yürüyüĢ tarzı dizinlerinin araĢtırılması ve denemelerinin yapılması üzerinde durulmuĢtur. Sayısal denemeler Casia B veritabanında mevcut değiĢik yürüyüĢ tarzları üzerinde çok sayıda denek üzerinde yapılmıĢtır. Bu veritabaında 11 farklı görünüm açılarından kaydedilen 124 kiĢi (31 kadın ve 93 erkek) bulunmaktadır. Her bir denek için, 6 nötr (Nu), 2 adet manto/palto giyme (CW) ve 2 adet çanta taĢıma (CB) olmak üzere 10 yürüme dizini bulunmaktadır. Önerilen yöntemin ilk bölümünde bir çerçeveli görüntüden arka planı çıkarma yöntemi kullanarak sırasal çerçeveli görüntüler ile arka planı arasındaki farkın hesaplaması üzerinde durulmuĢtur. Ġkinci bölümde YürüyüĢ Enerjisi (Gait Energy) görüntü özelliklikleri yardımıyla sınıflandırma yöntemi incelenmiĢtir. Son olarak bu çalıĢmada bir sınıflandırma aracı olarak Yürüme Enerjisi Görüntü (Gait Energy Image) ve Rastgele Yürüme Enerji Görüntü (Gait Entropy Enerji Image, GEnEI) yöntemlri uygulanmıĢtır. Wavelet Transformasyon tekniği ve GEnEI yöntemi kullanılarakveritabanından üç farklı yürüyüĢ tarzı özellikli görüntü grubu kurgulanmıĢtır. Bu yürüyüĢ tarzı özellikli görüntü grupları: (i) YaklaĢık Katsayı Rastgele Yürüme Enerji Görüntü (Approximate coefficient Energy Image, AGEnEI), (ii) Diksel Katsayı Rastgele Yürüme Enerji Görüntü (Vertical coefficient Energy Image, VGEnEI), ve (iii) her ikisinin birleĢkesi olan YaklaĢık ve Diksel Katsayı Yürüme Enerji Görüntü (Approximate coefficient Energy Image and Vertical coefficient Energy Image, AVGEnEI). Yukarıda belirtilen görüntüleme iĢlemlerinin iĢlevliliğinin denemesi için k-derece yakın komĢu (k-Neraest Neighboor, k-NN) ve destek vector makinası (Support vector Machine, SVM) olarak bilinen yöntemler önerilmiĢtir. Ayrıca yukarıda belirtilen üç tür enerji görüntü yöntemi birleĢtirme tabanlı karar verme (fuse-based decirion level fusion) yöntemi kullanılarak da denenmiĢtir. Sınıflandırmada k-NN yöntemi ile Nu gait dizinleri için AGEnEI % 97 lik ergitme seviyesini (fusion level), VGEnEI CB dizinleri için 91.4% lik ergitme seviyesini, ve AGEnEI CW dizinleri için %83.4 ergitme seviyesi sonuçları bulunmuĢtur. k=1, 3 ve 5 sayıları ile belirlenen üç ayrı özellik grubu arasında k=1 dikkate değer ergitme seviyesi sonuçları vermiĢtir. Her üç enerji görüntüleme yöntemi (Energy Entropy Image) „Decision-fusion‟ yöntemi ile birleĢtirildiğinde (fused) ergitme dereceleri Nu için %99.8, CB için %92.2 ve for CW için 86.3% dir. Bu sonuçlar her bir özelliğin ayrı ayrı ele alındığı durumunda elede edilen sonuçlardan daha iyi olduğu dikkate değerdir.

    Gender Identification System in Women-Only Parking Lots by Using Gait Analysis

    Get PDF
    There are a lot of problems faced in parking lots where the safety of women is insecure due to dark and quiet environment. Security personnel requires high cost and they tend to make errors or overlooked. Thus, a gender identification system was proposed to solve the matter. Human gait as a method of human recognition is not new in biometrics systems nowadays. In this project human gait is used for gender identification because of its advantages in this particular application, Gender Identification System in Women-only Parking Lots

    Human gait recognition under neutral and non-neutral gait sequences

    Get PDF
    Rapid advances in biometrics technology makes their use for person‘s identity more acceptable in a variety of applications, especially in the areas of the interest in security and surveillance. The upsurge in terrorist attacks in the past few years has focused research on biometric systems that have the ability to identify individuals from a distance, and this is spearheading research interest in Gait biometric due to being unobtrusive and less dependent on high image/video quality. Gait biometric is a behavioral trait that aims to identify individuals from image sequences based on their walking style. The growing list of possible civil as well as security applications for various purposes is paralleled by the emergence of a variety of research challenges in dealing with a various external as well as internal factors influencing the performance of Gait Recognition (GR) in unconstrained recording conditions. This thesis is concerned with Gait Recognition in unconstrained scenarios aims to address research questions covering (1) The selection of sets of features for a gait signature; (2) The effects of gender and/or recoding condition case (neutral, carrying a bag, coat wearing) on the performance of GR schemes; (3) Integrating gender and/or case classifications into GR; and (4) The role of emerging Kinect sensor technology, with its capability of sensing human skeletal features in GR and applications. Accordingly, our objectives will focus on investigating, developing and testing the performance of using a variety of gait sequencefeatures for the various components/tasks and their integration. Our tests are based on large number of experiments based on CASIA B database as well as an in-house database of Kinect sensor recording. In all experiments, we use different dimension reduction and feature selection methods do reduce the dimensions in these proposed feature vectors, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Fisher Score, followed by different classification methods like; k-nearest-neighbour (k-NN), Support Vector Machine (SVM), Naive Bayes and linear discriminant classifier (LDC), to test the performance of the proposed methods. The initial part is focused on reviewing existing background removal for indoor and outdoor scenarios and developing more efficient versions primarily by adopting the work for wavelet domain rather than the traditional spatial domain based schemes. These include motion detection by frame differencing and Mixture of Gaussians, the latter being more reliable for outdoor scenarios. Subsequently, we investigated a variety of features that can be extractedfrom various subbands of wavelet-decomposed frames of different body parts (partitioned according to the golden ratio). We gradually built sets of features, together with their fused combinations, that can categorized as hybrid of model-based and motion-based models. The first list of features developed to deal with Neutral Gait Recognition (NGR) includes: Spatio-Temporal Model (STM), Legs Motion Detection Feature (LMD), and the Statistical model of the approximation LL-wavelet subband images (AWM). We shall demonstrate that fusing these features achieves accuracy of 97%, which is comparable to the state of the art. These features will be shown to achieve 96% accuracy in gender classification (GC), and we shall establish that the NGR2 scheme that integrates GC into NGR improves the accuracy by a noticeable percentage. Testing the performance of these NGR schemes in recognising non-neutral cases revealed the challenges of Unrestricted Gait Recognition (UGR). The second part of the thesis is focused on developing UGR schemes. For this, first a new statistical wavelet feature set extracted from high frequency subbands, called Detail coefficients Wavelet Model (DWM) was added to the previous list. Using different combinations of these schemes, will be shown to significantly improve the performance for non-neutral gait cases, but to less extent in the coat wearing case. We then develop a Gait Sequence Case Detection (GSCD) which has excellent performance. We will show that integrating GSCD and GC together into UGR improves the performance for all cases. We shall also investigate the different UGS scheme that generalizes existing work on Gait Energy and Gait Entropy images (GEI and GEnI) features but in the wavelet domain and in different body parts. Testing these two schemes, and their fusion, post the PCA dimension reduction yield much improved accuracy for the non-neutral cases compared to existing scheme GEI and GEnI schemes, but are significantly outperformed by the last scheme. However, by fusing the UGS scheme with the GSCD+GC+UGR scheme above we will get best accuracy that outperform the state of the art in GR specially in the non-neutral cases. The thesis ended by conducting a rather limited investigation on the use of the Kinect sensors for GR. We develop two sets of features: Horizontal Distance Features and Vertical Distance Features from small set of skeleton point trajectories. The experimental result on neutral was very successful but for the unrestricted gait recognition (with the 5 case variations) satisfactory but not optimal performance relies on the gallery including balanced number of samples from all cases

    Gender Identification System in Women-Only Parking Lots by Using Gait Analysis

    Get PDF
    There are a lot of problems faced in parking lots where the safety of women is insecure due to dark and quiet environment. Security personnel requires high cost and they tend to make errors or overlooked. Thus, a gender identification system was proposed to solve the matter. Human gait as a method of human recognition is not new in biometrics systems nowadays. In this project human gait is used for gender identification because of its advantages in this particular application, Gender Identification System in Women-only Parking Lots

    Human Gait Analysis using Spatiotemporal Data Obtained from Gait Videos

    Get PDF
    Mit der Entwicklung von Deep-Learning-Techniken sind Deep-acNN-basierte Methoden zum Standard für Bildverarbeitungsaufgaben geworden, wie z. B. die Verfolgung menschlicher Bewegungen und Posenschätzung, die Erkennung menschlicher Aktivitäten und die Erkennung von Gesichtern. Deep-Learning-Techniken haben den Entwurf, die Implementierung und den Einsatz komplexer und vielfältiger Anwendungen verbessert, die nun in einer Vielzahl von Bereichen, einschließlich der Biomedizintechnik, eingesetzt werden. Die Anwendung von Computer-Vision-Techniken auf die medizinische Bild- und Videoanalyse hat zu bemerkenswerten Ergebnissen bei der Erkennung von Ereignissen geführt. Die eingebaute Fähigkeit von convolutional neural network (CNN), Merkmale aus komplexen medizinischen Bildern zu extrahieren, hat in Verbindung mit der Fähigkeit von long short term memory network (LSTM), die zeitlichen Informationen zwischen Ereignissen zu erhalten, viele neue Horizonte für die medizinische Forschung geschaffen. Der Gang ist einer der kritischen physiologischen Bereiche, der viele Störungen im Zusammenhang mit Alterung und Neurodegeneration widerspiegeln kann. Eine umfassende und genaue Ganganalyse kann Einblicke in die physiologischen Bedingungen des Menschen geben. Bestehende Ganganalyseverfahren erfordern eine spezielle Umgebung, komplexe medizinische Geräte und geschultes Personal für die Erfassung der Gangdaten. Im Falle von tragbaren Systemen kann ein solches System die kognitiven Fähigkeiten beeinträchtigen und für die Patienten unangenehm sein. Außerdem wurde berichtet, dass die Patienten in der Regel versuchen, während des Labortests bessere Leistungen zu erbringen, was möglicherweise nicht ihrem tatsächlichen Gang entspricht. Trotz technologischer Fortschritte stoßen wir bei der Messung des menschlichen Gehens in klinischen und Laborumgebungen nach wie vor an Grenzen. Der Einsatz aktueller Ganganalyseverfahren ist nach wie vor teuer und zeitaufwändig und erschwert den Zugang zu Spezialgeräten und Fachwissen. Daher ist es zwingend erforderlich, über Methoden zu verfügen, die langfristige Daten über den Gesundheitszustand des Patienten liefern, ohne doppelte kognitive Aufgaben oder Unannehmlichkeiten bei der Verwendung tragbarer Sensoren. In dieser Arbeit wird daher eine einfache, leicht zu implementierende und kostengünstige Methode zur Erfassung von Gangdaten vorgeschlagen. Diese Methode basiert auf der Aufnahme von Gehvideos mit einer Smartphone-Kamera in einer häuslichen Umgebung unter freien Bedingungen. Deep neural network (NN) verarbeitet dann diese Videos, um die Gangereignisse zu extrahieren. Die erkannten Ereignisse werden dann weiter verwendet, um verschiedene räumlich-zeitliche Parameter des Gangs zu quantifizieren, die für jedes Ganganalysesystem wichtig sind. In dieser Arbeit wurden Gangvideos verwendet, die mit einer Smartphone-Kamera mit geringer Auflösung außerhalb der Laborumgebung aufgenommen wurden. Viele Deep- Learning-basierte NNs wurden implementiert, um die grundlegenden Gangereignisse wie die Fußposition in Bezug auf den Boden aus diesen Videos zu erkennen. In der ersten Studie wurde die Architektur von AlexNet verwendet, um das Modell anhand von Gehvideos und öffentlich verfügbaren Datensätzen von Grund auf zu trainieren. Mit diesem Modell wurde eine Gesamtgenauigkeit von 74% erreicht. Im nächsten Schritt wurde jedoch die LSTM-Schicht in dieselbe Architektur integriert. Die eingebaute Fähigkeit von LSTM in Bezug auf die zeitliche Information führte zu einer verbesserten Vorhersage der Etiketten für die Fußposition, und es wurde eine Genauigkeit von 91% erreicht. Allerdings gibt es Schwierigkeiten bei der Vorhersage der richtigen Bezeichnungen in der letzten Phase des Schwungs und der Standphase jedes Fußes. Im nächsten Schritt wird das Transfer-Lernen eingesetzt, um die Vorteile von bereits trainierten tiefen NNs zu nutzen, indem vortrainierte Gewichte verwendet werden. Zwei bekannte Modelle, inceptionresnetv2 (IRNV-2) und densenet201 (DN-201), wurden mit ihren gelernten Gewichten für das erneute Training des NN auf neuen Daten verwendet. Das auf Transfer-Lernen basierende vortrainierte NN verbesserte die Vorhersage von Kennzeichnungen für verschiedene Fußpositionen. Es reduzierte insbesondere die Schwankungen in den Vorhersagen in der letzten Phase des Gangschwungs und der Standphase. Bei der Vorhersage der Klassenbezeichnungen der Testdaten wurde eine Genauigkeit von 94% erreicht. Da die Abweichung bei der Vorhersage des wahren Labels hauptsächlich ein Bild betrug, konnte sie bei einer Bildrate von 30 Bildern pro Sekunde ignoriert werden. Die vorhergesagten Markierungen wurden verwendet, um verschiedene räumlich-zeitliche Parameter des Gangs zu extrahieren, die für jedes Ganganalysesystem entscheidend sind. Insgesamt wurden 12 Gangparameter quantifiziert und mit der durch Beobachtungsmethoden gewonnenen Grundwahrheit verglichen. Die NN-basierten räumlich-zeitlichen Parameter zeigten eine hohe Korrelation mit der Grundwahrheit, und in einigen Fällen wurde eine sehr hohe Korrelation erzielt. Die Ergebnisse belegen die Nützlichkeit der vorgeschlagenen Methode. DerWert des Parameters über die Zeit ergab eine Zeitreihe, eine langfristige Darstellung des Ganges. Diese Zeitreihe konnte mit verschiedenen mathematischen Methoden weiter analysiert werden. Als dritter Beitrag in dieser Dissertation wurden Verbesserungen an den bestehenden mathematischen Methoden der Zeitreihenanalyse von zeitlichen Gangdaten vorgeschlagen. Zu diesem Zweck werden zwei Verfeinerungen bestehender entropiebasierter Methoden zur Analyse von Schrittintervall-Zeitreihen vorgeschlagen. Diese Verfeinerungen wurden an Schrittintervall-Zeitseriendaten von normalen und neurodegenerativen Erkrankungen validiert, die aus der öffentlich zugänglichen Datenbank PhysioNet heruntergeladen wurden. Die Ergebnisse zeigten, dass die von uns vorgeschlagene Methode eine klare Trennung zwischen gesunden und kranken Gruppen ermöglicht. In Zukunft könnten fortschrittliche medizinische Unterstützungssysteme, die künstliche Intelligenz nutzen und von den hier vorgestellten Methoden abgeleitet sind, Ärzte bei der Diagnose und langfristigen Überwachung des Gangs von Patienten unterstützen und so die klinische Arbeitsbelastung verringern und die Patientensicherheit verbessern

    Pedestrian soft biometrics recognition using deep learning on thermal images in smart cities

    Get PDF
    With technological advancement and the rise of the Internet of Things, our society is becoming more interconnected than ever before. Our computers and devices are getting smaller, and their computing power and memory has been increasing. These advances coupled with the leaps in artificial intelligence caused by the deep learning revolution in recent yearshave led to an increasingly rising interest in the field of pervasive intelligence. Intelligence in the environment has been used in smart homes in order to bring assistance to semi-autonomous people by performing activity recognition based on sensor data. As technology keeps improving, we may start to investigate the extension of assistive technologies beyond the boundaries of smart homes and into our smart cities. In order to bring assistance to semi-autonomous people, the first step is to be able to recognize profiles of vulnerable people. In order to leverage technology and artificial intelligence to make our cities smarter, safer and more accessible, this thesis investigates the use of environmental sensors such as thermal cameras to perform pedestrian soft biometrics recognition (age, gender and mobility) in the city. In this thesis, the process of building prototypes from scratch in order to collect thermal gait data in the city is explored, and the use and optimization of deep learning algorithms to perform soft biometrics recognition, as well as the feasibility of implementing these algorithms on limited resource boards are explored. The use of unprocessed thermal images allows a higher degree of privacy for the citizens, and it is novel in the field of human profile recognition. This thesis aims to set the foundation of future work, both in the field of thermal images-based soft biometrics recognition and pervasive intelligence in our cities in order to make them smarter, and move towards an interconnected society. Les progrès technologiques et le développement de l’Internet des Objets nous mènent vers une société de plus en plus interconnectée. Nos ordinateurs et nos appareils deviennent de plus en plus petits et leur puissance de calcul et leur mémoire ne cesse de s’améliorer. Ces avancées combinées aux récents progrès dans le domaine de l’intelligence artificielle avec la révolution de l’apprentissage profond ont mené à un intérêt grandissant dans le domaine de l’intelligence ambiante. L’intelligence ambiante a été utilisée dans le domaine des maisons intelligentes sous forme de reconnaissance d’activités, permettant d’assister les personnes semi-autonomes en utilisant des données collectées par des capteurs. Alors que le progrès technologique continue, nous arrivons à un point où l’hypothèse d’étendre ces stratégies d’assistance des maisons aux villes intelligentes devient de plus en plus réaliste. Afin d’étendre cette assistance aux villes, la première étape est d’identifier les personnes vulnérables, qui sont celles qui pourraient bénéficier de cette assistance. Dans le but d’utiliser la technologie pour rendre nos villes plus intelligentes, plus sûres et plus accessibles, cette thèse explore l’utilisation de capteurs environnementaux tels que des caméras thermiques pour effectuer de la reconnaissance de profils dans la ville (âge, genre et mobilité). Dans cette thèse, le processus de construction de prototypes pour récolter des données thermales dans la ville est présenté, et l’utilisation ainsi que l’optimisation d’algorithmes d’apprentissage profond pour la reconnaissance de profils est explorée. L’implémentation des algorithmes sur un système embarqué est également abordée. L’utilisation d’images thermiques garantit un plus grand degré d’anonymat pour les citoyens que l’utilisation de caméras RGB, et cette thèse représente les premiers travaux de reconnaissance de profils multiples en utilisant uniquement des images thermiques sans pré-traitement. Cette thèse a pour objectif de poser les bases pour des travaux futurs dans le domaine de la reconnaissance de profils en utilisant des images thermiques, ainsi que dans le domaine de l’intelligence ambiante dans nos villes, afin de les rendre plus intelligentes et de se diriger vers une société interconnectée

    Data-independent vs. data-dependent dimension reduction for pattern recognition in high dimensional spaces

    Get PDF
    There has been a rapid emergence of new pattern recognition/classification techniques in a variety of real world applications over the last few decades. In most of the pattern recognition/classification applications, the pattern of interest is modelled by a data vector/array of very high dimension. The main challenges in such applications are related to the efficiency of retrieval, analysis, and verifying/classifying the pattern/object of interest. The “Curse of Dimension” is a reference to these challenges and is commonly addressed by Dimension Reduction (DR) techniques. Several DR techniques has been developed and implemented in a variety of applications. The most common DR schemes are dependent on a dataset of “typical samples” (e.g. the Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA)). However, data-independent DR schemes (e.g. Discrete Wavelet Transform (DWT), and Random Projections (RP)) are becoming more desirable due to lack of density ratio of samples to dimension. In this thesis, we critically review both types of techniques, and highlight advantages and disadvantages in terms of efficiency and impact on recognition accuracy. We shall study the theoretical justification for the existence of DR transforms that preserve, within tolerable error, distances between would be feature vectors modelling objects of interest. We observe that data-dependent DRs do not specifically attempts to preserve distances, and the problems of overfitting and biasness are consequences of low density ratio of samples to dimension. Accordingly, the focus of our investigations is more on data-independent DR schemes and in particular on the different ways of generating RPs as an efficient DR tool. RPs suitable for pattern recognition applications are only restricted by a lower bound on the reduced dimension that depends on the tolerable error. Besides, the known RPs that are generated in accordance to some probability distributions, we investigate and test the performance of differently constructed over-complete Hadamard mxn (m<<n) submatrices, using the inductive Sylvester and Walsh-Paley methods. Our experimental work conducted for 2 case studies (Speech Emotion Recognition (SER) and Gait-based Gender Classification (GBGC)) demonstrate that these matrices perform as well, if not better, than data-dependent DR schemes. Moreover, dictionaries obtained by sampling the top rows of Walsh Paley matrices outperform matrices constructed more randomly but this may be influenced by the type of biometric and/or recognition schemes. We shall, also propose the feature-block (FB) based DR as an innovative way to overcome the problem of low density ratio applications and demonstrate its success for the SER case study
    corecore