Human gait recognition: viewing angle effect on normal walking pattern

Gait recognition has recently gained interest of researchers as it performs identification of subjects at a distance from the camera. However, due to the changes in the viewing angles, it gets cumbersome for a system to perform recognition based on the walking pattern of an individual. In this work, the aim is to propose a simple baseline method for the purpose of human recognition based on the shape of its body and walking pattern when the subject is observed from different viewing angles. The recognition is also tested on the subjects in a scenario where the individual subjects are registered while walking in normal walking pattern followed by the testing in normal walking mode, apart from being observed at different viewing angles. Gait periodicity is estimated after extracting the silhouettes of an individual, followed by obtaining the total silhouette representation of an individual using Matlab. The total silhouette representations obtained from the probe gait data are compared to the gallery gait data representations for the purpose of similarity computation by calculating the RMS value between the said representations. Higher the value, lesser is the similarity & vice versa

Evaluation of the effect of walking speeds on human gait recognition

Human Gait Recognition is typically alluded to imply the human ID by the style/way individuals stroll in picture sequences. Our point is to execute the customary gait recognition calculation and to demonstrate the variety in recognition when subject is watched parallel to camera under three conditions- walking slow, at typical speed and walking quickly. For this situation, the work devises a novel strategy with the end goal of likeness calculation as opposed to the customary idea, where the overall recognition rate of 65% percent was achieved

Human gait recognition and classification using similarity index for various conditions

Gait recognition is usually referred to signify the human identification by the style/way people walk in image sequences. Our aim is to implement the traditional gait recognition algorithm and to show the variation in gait recognition when subject is observed parallel to camera under three conditions- walking normal, carrying a bag and wearing a coat. However in this case, the work devises a novel method for the purpose of similarity computation rather than the traditional recognition where the overall recognition rate of 78.57 percent was obtained

Human gait recognition and classification using similarity index for various conditions

Gait recognition is usually referred to signify the human identification by the style/way people walk in image sequences. Our aim is to implement the traditional gait recognition algorithm and to show the variation in gait recognition when subject is observed parallel to camera under three conditions- walking normal, carrying a bag and wearing a coat. However in this case, the work devises a novel method for the purpose of similarity computation rather than the traditional recognition where the overall recognition rate of 78.57 percent was obtained

Gait recognition and effect of noise on the recognition rate

Gait is defined to be the coordinated, cyclic combination of movements that result in human locomotion. Gait recognition refers to identify a human by the style/way, a person walks in image sequences. The objective of this paper is to implement the traditional gait recognition algorithm and to show the variation in gait recognition when subject is observed parallel to camera under three conditions- walking normal, carrying a bag and wearing a coat. In this work, we are devising a novel method for the purpose of similarity computation rather than the traditional recognition where 78.57% of overall recognition rate was obtained. Furthermore, noise is introduced to evaluate its effect on the recognition rate. Keywords—Human gait, biometrics, human identification, gait databases, silhouettes