Gait Recognition

Gait recognition has received increasing attention as a remote biometric identification technology, i.e. it can achieve identification at the long distance that few other identification technologies can work. It shows enormous potential to apply in the field of criminal investigation, medical treatment, identity recognition, human‐computer interaction and so on. In this chapter, we introduce the state‐of‐the‐art gait recognition techniques, which include 3D‐based and 2D‐based methods, in the first part. And considering the advantages of 3D‐based methods, their related datasets are introduced as well as our gait database with both 2D silhouette images and 3D joints information in the second part. Given our gait dataset, a human walking model and the corresponding static and dynamic feature extraction are presented, which are verified to be view‐invariant, in the third part. And some gait‐based applications are introduced

Autenticación multifactor con el uso de un sensor kinect

In this paper a multifactor authentication system by using Kinect sensor and computer equipment was developed. It was used the C# language for coding with the development  environment  and tools  provided by  the  manufacturer  for  Windows  operating  system,  to choose a combination of authentication methods to reduce  the  ability  of  a  non-authorized  user  to  be  eligible to access a certain place or system. Five methods were chosen to  obtain  the  multifactor authentication,  covering the three categories of authentication methods: Information keys, physical keys and biometric keys, based respectively in something the person knows, something the person has and something the person is. A reliable, robust and easy to use authentication system was achieved, favoring the reliability and reducing the complexity of each of the individual methods. It proved to be possible to develop a multifactor authentication system with Kinect sensor.En este trabajo se desarrolló un sistema de autenticación multifactor empleando el sensor Kinect y equipo de cómputo. Se utilizó programación en lenguaje C# con el ambiente de desarrollo y las herramientas que provee el fabricante para el sistema operativo Windows. Se eligió una combinación de métodos de autenticación con el fin de reducir la capacidad que tiene un usuario no autorizado de ser elegible para tener acceso a un determinado sistema o lugar. Se seleccionaron cinco métodos para conseguir la autenticación multifactor, cubriendo las tres categorías de métodos de autenticación: Llaves de información, Llaves físicas y Llaves biométricas, basadas respectivamente en algo que la persona sabe, algo que la persona posee y algo que la persona es. Se consiguió un sistema de autenticación confiable, robusto y sencillo de usar, privilegiando la confiabilidad y disminuyendo la complejidad de cada uno de los métodos individuales. Se demostró que es posible desarrollar un sistema de autenticación multifactor con un sensor Kinect

Using Skeleton Correction to Improve Flash Lidar-Based Gait Recognition

This paper presents GlidarPoly, an efficacious pipeline of 3D gait recognition for flash lidar data based on pose estimation and robust correction of erroneous and missing joint measurements. A flash lidar can provide new opportunities for gait recognition through a fast acquisition of depth and intensity data over an extended range of distance. However, the flash lidar data are plagued by artifacts, outliers, noise, and sometimes missing measurements, which negatively affects the performance of existing analytics solutions. We present a filtering mechanism that corrects noisy and missing skeleton joint measurements to improve gait recognition. Furthermore, robust statistics are integrated with conventional feature moments to encode the dynamics of the motion. As a comparison, length-based and vector-based features extracted from the noisy skeletons are investigated for outlier removal. Experimental results illustrate the superiority of the proposed methodology in improving gait recognition given noisy, low-resolution flash lidar data

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