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An Assessment Study of Gait Biometric Recognition Using Machine Learning
Abstract
The biometric gait recognition system has attracted much attention in the computer vision community because of its discreet recognition advantage in relatively distant locations. The present study provides a comprehensive overview of recent developments in approaches to detecting walking. The survey focuses on three main themes of a general walk-detection system, namely the appearance of the image of walking, the reduction in the dimensionality of features, and the classification of walking, and in addition, a review of the available records or datasets for the public to process the gait reorganization using different methods and calcifications. The final discussions describe a series of research challenges and give promising pointers for the future on the ground.
The integration of the latest breakthroughs in bioinformatics technology from one side and artificial intelligence from another side, enables remarkable advances in the fields of intelligent security guard computational biology, healthcare, and so on. Among them, biometrics based automatic human identification is one of the most fundamental and significant research topic. Human gait, which is a biometric features with the unique capability, has gained significant attentions as the remarkable characteristics of remote accessed, robust and security in the biometrics based human identification. However, the existed methods cannot well handle the indistinctive inter-class differences and large intra-class variations of human gait in real-world situation. In this paper, we have developed an efficient spatial-temporal gait features with deep learning for human identification. First of all, we proposed a gait energy image (GEI) based Siamese neural network to automatically extract robust and discriminative spatial gait features for human identification. Furthermore, we exploit the deep 3-dimensional convolutional networks to learn the human gait convolutional 3D (C3D) as the temporal gait features. Finally, the GEI and C3D gait features are embedded into the null space by the Null Foley-Sammon Transform (NFST). In the new space, the spatial-temporal features are sufficiently combined with distance metric learning to drive the similarity metric to be small for pairs of gait from the same person, and large for pairs from different persons. Consequently, the experiments on the world’s largest gait database show our framework impressively outperforms state-of-the-art methods.
The research work was conducted with the objectives to find the intricacies faced by students, research practitioners and professionals during their research and to suggest the best software alternatives for each category of problems faced by them, based on expected characteristics. The study is intended to help researchers from diverse fields, especially those not belonging to information technology and its allied background.
This study was pursued by the researcher collecting firsthand information for primary data through a face-to face interview of students, research practitioners and professionals from diverse fields using a structured open ended questionnaire querying about the intricacies faced by them during their research and the characteristics expected by them in the software aiding in research. The analysis is performed in three stages; at the first stage the researcher performs thematic textual analysis on the responses to the questionnaire, short listing the intricacies faced by researchers during their research and the characteristics expected by them in the software aiding in research. Secondly, a keyword search is performed on the shortlisted intricacies to find the categories of software required to aid the researchers for each type of problem faced by them and then find software alternatives for each category of software along with a brief functional minutiae. Finally the researcher conducts a thorough study of the related literature, websites, brochure of software companies as well as their products and services and also applies observation and application method examining the various short listed software alternatives for each category based on expected characteristics to suggests the best software alternatives for each category of intricacies faced by researchers.
Evolutionary decision fusion has applications in biometric authentication and verification. Grey Wolf Optimizer (GWO) is one such evolutionary decision fusion approach that can be used to tune the fusion parameters in a multimodal data acquisition system. Human gait is a proven biometric trait with applications in security and authentication. However, acquiring human gait data can be erroneous due to various factors and multimodal fusion of such erroneous gait data can be challenging. In this paper, we propose a new decision fusion-based approach to solve the above problem. Gait data is recorded simultaneously using motion sensors and visible light camera. The signals of the motion sensors are modeled using a Long Short Term Memory (LSTM) neural network and corresponding video recordings are processed using a 3D Convolutional Neural Network (CNN). GWO has been used to optimize the parameters during fusion. It has been chosen based on the underlying hunting strategy that leads to better approximation of the solution and interestingly, in our case it converges quicker than other optimization techniques such as Genetic Algorithm (GA) or Particle Swarm Optimization (PSO). To test the model, a dataset involving 23 males and females have been recorded while they perform four different types of walks including, normal-walk, fast-walk, walking while listening to music, and walking while watching multimedia content on a mobile. An overall accuracy of 91.3% has been recorded across all test scenarios. Results reveal that the proposed study can further be explored to design robust gait biometric systems.
Gait recognition is a biometric technique used in determining the identity of humans based on the style and the manner of their walk. Its performance is often degraded by covariate factors such as carrying condition changes, clothing condition changes, and viewing angle variations. Recently, machine learning based techniques have produced promising results for challenging classification problems. Since, a deep convolutional neural network (CNN) is one of the most advanced machine learning techniques with the ability to approximate complex non-linear functions, we develop a specialized deep CNN architecture for Gait Recognition. The proposed architecture is less sensitive to several cases of the common variations and occlusions that affect and degrade gait recognition performance. It can also handle relatively small data sets without using any augmentation or fine-tuning techniques. The majority of previous approaches to gait recognition have used subspace learning methods which have several shortcomings that we avoid. Our specialized deep CNN model can obtain competitive performance when tested on the CASIA-B large gait dataset.
Recognizing people by gait has a unique advantage over other biometrics: it has potential for use at a distance when other biometrics might be at too low a resolution, or might be obscured. The current state of the art can achieve over 90% identification rate under situations where the training and test data are captured under similar conditions, while recognition rates with change of clothing, shoe, surface, illumination, and pose usually decrease performance and are the subject of much of the current study. Recognition can be achieved on outdoor data with uncontrolled illumination and at a distance when other biometrics could not be used. We shall show how this position has been achieved, covering most approaches to recognition by gait and the databases on which performance has been evaluated. We shall describe the context of these approaches, show how recognition by gait can be achieved and how current limits on performance are understood. We shall describe results on the most popular database, showing how recognition can handle some of the covariates that can affect recognition. We shall also investigate the supporting literature for this research, since the notion that people can be recognized by gait has support not only in medicine and biomedicine, and also in literature and psychology and other areas. In this way, we shall show that this new biometric has capability and research and application potential in other domains.
There are several confounding factors that can reduce the accuracy of gait recognition systems. These factors can reduce the distinctiveness, or alter the features used to characterise gait, they include variations in clothing, lighting, pose and environment, such as the walking surface. Full invariance to all confounding factors is challenging in the absence of high-quality labelled training data. We introduce a simulation-based methodology and a subject-specific dataset which can be used for generating synthetic video frames and sequences for data augmentation. With this methodology, we generated a multi-modal dataset. In addition, we supply simulation files that provide the ability to simultaneously sample from several confounding variables. The basis of the data is real motion capture data of subjects walking and running on a treadmill at different speeds. Results from gait recognition experiments suggest that information about the identity of subjects is retained within synthetically generated examples. The dataset and methodology allow studies into fully-invariant identity recognition spanning a far greater number of observation conditions than would otherwise be possible.
View variation is one of the greatest challenges faced by the gait recognition research community. Recently, there are studies that model sets of gait features from multiple views as linear subspaces, which are known to form a special manifold called the Grassmann manifold. Conjecturing that modeling via linear subspace representation is not completely sufficient for gait recognition across view change, we take a step forward to consider non-linear subspace representation. A collection of multi-view gait features encapsulated in the form of a linear subspace is projected to the non-linear subspace through the expansion coefficients induced by kernel principal component analysis. Since subspace representation is inherently non-Euclidean, naïve vectorization as input to the vector-based pattern analysis machines is expected to yield suboptimal accuracy performance. We deal with this difficulty by embedding the manifold in a Reproducing Kernel Hilbert Space (RKHS) through a positive definite kernel function defined on the Grassmann manifold. A closer examination reveals that the proposed approach can actually be interpreted as a doubly-kernel method. To be specific, the first kernel maps the linear subspace representation non-linearly to a feature space; while the second kernel permits the application of kernelization-enabled machines established for vector-based data on the manifold-valued multi-view gait features. Experiments on the CASIA gait database shows that the proposed doubly-kernel method is effective against view change in gait recognition.