Model-based 3d gait biometric using quadruple fusion classifier

The area of gait biometrics has received significant interest in the last few years, largely due to the unique suitability and reliability of gait pattern as a human recognition technique. The advantage of gait over other biometrics is that it can perform non-intrusive data acquisition and can be captured from a distance. Current gait analysis approach can be divided into model-free and model-based approach. The gait data extracted for identification process may be influenced by ambient noise conditions, occlusion, changes in backgrounds and illumination when model-free 2D silhouette data is considered. In addition, the performance in gait biometric recognition is often affected by covariate factors such as walking condition and footwear. These are often related to low performance of personal verification and identification. While body biometrics constituted of both static and dynamics features of gait motion, they can complement one another when used jointly to maximise recognition performance. Therefore, this research proposes a model-based technique that can overcome the above limitations. The proposed technique covers the process of extracting a set of 3D static and dynamic gait features from the 3D skeleton data in different covariate factors such as different footwear and walking condition. A skeleton model from forty subjects was acquired using Kinect which was able to provide human skeleton and 3D joints and the features were extracted and categorized into static and dynamic data. Normalization process was performed to scale down the features into a specific range of structure, followed by feature selection process to select the most significant features to be used in classification. The features were classified separately using five classification algorithms for static and dynamic features. A new fusion framework is proposed based on score level fusion called Quadruple Fusion Framework (QFF) in order to combine the static and dynamic features obtained from the classification model. The experimental result of static and dynamic fusion achieved the accuracy of 99.5% for footwear covariates and 97% for walking condition covariates. The result of the experimental validation demonstrated the viability of gait as biometrics in human recognition

Anomalous behaviour detection using heterogeneous data

Anomaly detection is one of the most important methods to process and find abnormal data, as this method can distinguish between normal and abnormal behaviour. Anomaly detection has been applied in many areas such as the medical sector, fraud detection in finance, fault detection in machines, intrusion detection in networks, surveillance systems for security, as well as forensic investigations. Abnormal behaviour can give information or answer questions when an investigator is performing an investigation. Anomaly detection is one way to simplify big data by focusing on data that have been grouped or clustered by the anomaly detection method. Forensic data usually consists of heterogeneous data which have several data forms or types such as qualitative or quantitative, structured or unstructured, and primary or secondary. For example, when a crime takes place, the evidence can be in the form of various types of data. The combination of all the data types can produce rich information insights. Nowadays, data has become ‘big’ because it is generated every second of every day and processing has become time-consuming and tedious. Therefore, in this study, a new method to detect abnormal behaviour is proposed using heterogeneous data and combining the data using data fusion technique. Vast challenge data and image data are applied to demonstrate the heterogeneous data. The first contribution in this study is applying the heterogeneous data to detect an anomaly. The recently introduced anomaly detection technique which is known as Empirical Data Analytics (EDA) is applied to detect the abnormal behaviour based on the data sets. Standardised eccentricity (a newly introduced within EDA measure offering a new simplified form of the well-known Chebyshev Inequality) can be applied to any data distribution. Then, the second contribution is applying image data. The image data is processed using pre-trained deep learning network, and classification is done using a support vector machine (SVM). After that, the last contribution is combining anomaly result from heterogeneous data and image recognition using new data fusion technique. There are five types of data with three different modalities and different dimensionalities. The data cannot be simply combined and integrated. Therefore, the new data fusion technique first analyses the abnormality in each data type separately and determines the degree of suspicious between 0 and 1 and sums up all the degrees of suspicion data afterwards. This method is not intended to be a fully automatic system that resolves investigations, which would likely be unacceptable in any case. The aim is rather to simplify the role of the humans so that they can focus on a small number of cases to be looked in more detail. The proposed approach does simplify the processing of such huge amounts of data. Later, this method can assist human experts in their investigations and making final decisions

Extração e classificação dos parâmetros do corpo humano para análise e reconhecimento da marcha

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2017.A análise da marcha humana é considerada como uma nova ferramenta biométrica pela capacidade de obter as métricas do corpo à distância. Os identificadores biométricos possuem propriedades que tecnologicamente podem medir e analisar as características do corpo humano, utilizados como forma de identificação e controle de acesso para segurança. O reconhecimento através da apropriada interpretação dos parâmetros da marcha é um problema importante para classificação de padrões. Este trabalho possui como finalidade desenvolver um sistema de processamento de imagens que seja capaz de extrair padrões do movimento para a análise da marcha e apresentar um diagnóstico comparativo entre diferentes tipos de redes neurais, para a aplicação de técnicas que possam determinar a qualidade e eficácia das estatísticas para a identificação humana. Para este objetivo, utilizou-se dados de voluntários a partir do aplicativo desenvolvido em C# com base na análise tridimensional feita pela câmera Kinect da Microsoft, onde é possível identificar o esqueleto humano e extrair automaticamente os parâmetros cinéticos e cinemáticos. Os resultados obtidos revelaram a viabilidade para o processo de extração dos parâmetros da marcha e do reconhecimento do corpo humano.Abstract : The analysis of human gait is considered as a new biometric tool for the ability to obtain the metrics of the body at a distance. Biometric identifiers have properties that technology can measure and analyze the characteristics of the human body, used as a form of identification and access control for security. The recognition through suitable interpretation of parameters of the gait is a major problem for pattern classification. This work has as purpose to develop an image processing system that is able to extract patterns of movement for gait analysis and to present a comparative diagnosis between different types of neural networks, for applying techniques that can to determine the quality and efficacy of the statistics for human identification. For this objective, we used data from volunteers from the application developed in C# based on three-dimensional analysis made by Microsoft's Kinect camera, where it is possible to identify the human skeleton and automatically extract the kinetic and kinematic parameters. The results obtained proved the feasibility to extraction process of gait parameters and the recognition of the human body