Human Gait Recognition System Research Articles

Principal component analysis for human gait recognition system

This paper represents a method for Human Recognition system using Principal Component Analysis. Human Gait recognition works on the gait of walking subjects to identify people without them knowing or without their permission. The initial step in this kind of system is to generate silhouette frames of walking human. A number of features couldb be exytacted from these frames such as centriod ratio, heifht, width and orientation. The Principal Component Analysis (PCA) is used for the extracted features to condense the information and produces the main components that can represent the gait sequences for each waiking human. In the testing phase, the generated gait sequences are recognized by using a minimum distance classifier based on eluclidean distance matched with the one that already exist in the database used to identify walking subject.

Principal component analysis for human gait recognition system

Principal component analysis for human gait recognition system

Human Gait Recognition System Based on Support Vector Machine Algorithm and Using Wearable Sensors

Human Gait Recognition System Based on Support Vector Machine Algorithm and Using Wearable Sensors

Vision based Human Gait Recognition System: Observations, Pragmatic Conditions and Datasets

Background: Gait uniquely distinguishes the persons by using their physiological and psychological state. For this reason, human gait recognition at a distance recently gained wider interest from research community. In recent days, the concept of fusion based biometric systems has attracted many researchers and it is proved that gait can be valuable solution when all other biometric sources failed. However gait is still open research area. Method: The review explains the observations with the help of available data and scientific arguments of the experts in their documented works are also taken for our study. Findings: This paper provides a comprehensive survey of current developments on gait recognition approaches and emphasizes on three major phases involved in gait recognition system, namely representation, dimensionality reduction and classification. We also discuss in detail the pragmatic conditions of gait and efficient utilization of standard datasets in order to use gait recognition systems in masses. Also we highlighted the prominent guidelines to internal and external gait challenges. Further we provide the motivations for fusion of physiological and psychological biometric sources to accomplish practical scenarios. Application/Improvement: The study concludes that all previous attempts are restricted to only few external gait variations and evaluated on limited data set. However, there is no single attempt which addresses most of the external gait variations. Hence there is a scope for further exploration and evaluation.

Human gait recognition system based on shadow free silhouettes using truncated singular value decomposition transformation model

In present scenario biometrics system are getting popular day by day and they are classified based on subject's cooperation and non-cooperation nature. Gait biometrics is one of the popularly traits used for person identification. The gait biometrics has an edge over the other biometrics traits as it works well even if the subject is not cooperative. The multiple problems such as shadow detection, removal of moving subjects in visual surveillance, less number of gallery probes in different conditions and existence of multiple views in the data are encountered due to surveillance camera and affect the performance of the gait recognition system. In this paper, we used three algorithms to overcome these problems up to an extent. In first algorithm, photometric properties based method is used to remove shadows at the time of silhouette generation from the recorded video. Then, an algorithm for synthetic gait energy image (GEI) templates generation employed to increase the corresponding gallery probes dataset and finally, singular value decomposition transformation is applied to transform the gait feature from one view to another. The performance of the proposed algorithm is experimentally validated on a benchmark dataset of indoor as well as outdoor video sequences by comparing it with the existing algorithms.

A Novel Human Gait Recognition System

In this paper considering a n e w human gait recognition system based on Radon transform which gives a high precision recognition rate. Innovation of this paper allocate to feature extraction and usage of them during process by combined neural networks . feature extraction is based on the Radon transform of binary silhouettes .in this paper For each gait sequence, the transformed silhouettes are used after background estimation and human detection in the scene to make each related template's. then set of all templates is used to subspace projection by following PCA method and earning final decimated feature vector for each persons in database. consequently earned feature vector for each person's is applied to multilayer perceptron neural network and set of all neural networks feed to final neural network for final decision . Experimental results is performed over a suitable data base include 10 samples for ten person which each sample have 130 frames approximately. 97% recognition rate of the proposed system is obtained over 10 samples test patterns.