Gait recognition under carrying condition : a static dynamic fusion method

When an individual carries an object, such as a briefcase, conventional gait recognition algorithms based on average silhouette/Gait Energy Image (GEI) do not always perform well as the object carried may have the potential of being mistakenly regarded as a part of the human body. To solve such a problem, in this paper, instead of directly applying GEI to represent the gait information, we propose a novel dynamic feature template for classification. Based on this extracted dynamic information and some static feature templates (i.e., head part and trunk part), we cast gait recognition on the large USF (University of South Florida) database by adopting a static/dynamic fusion strategy. For the experiments involving carrying condition covariate, significant improvements are achieved when compared with other classic algorithms

GRIDDS - A Gait Recognition Image and Depth Dataset

Several approaches based on human gait have been proposed in the literature, either for medical research reasons, smart surveillance, human-machine interaction, or other purposes, whose validation highly depends on the access to common input data through available datasets, enabling a coherent performance comparison. The advent of depth sensors leveraged the emergence of novel approaches and, consequently, the usage of new datasets. In this work we present the GRIDDS - A Gait Recognition Image and Depth Dataset, a new and publicly available gait depth-based dataset that can be used mostly for person and gender recognition purposes. (c) Springer Nature Switzerland AG 2019

A novel approach of gait recognition through fusion with footstep information

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. R. Vera-Rodríguez, J. Fiérrez, J. S.D. Mason, J. Ortega-García, "A novel approach of gait recognition through fusion with footstep information" in International Conference on Biometrics (ICB), Madrid (Spain), 2013, 1-6This paper is focused on two biometric modes which are very linked together: gait and footstep biometrics. Footstep recognition is a relatively new biometric based on signals extracted from floor sensors, while gait has been more researched and it is based on video sequences of people walking. This paper reports a directly comparative assessment of both biometrics using the same database (SFootBD) and experimental protocols. A fusion of the two modes leads to an enhanced gait recognition performance, as the information from both modes comes from different capturing devices and is not very correlated. This fusion could find application in indoor scenarios where a gait recognition system is present, such as in security access (e.g. security gate at airports) or smart homes. Gait and footstep systems achieve results of 8.4% and 10.7% EER respectively, which can be significantly improved to 4.8% EER with their fusion at the score level into a walking biometric.This work has been partially supported by projects Bio-Shield (TEC2012-34881), Contexts (S2009/TIC-1485), TeraSense (CSD2008-00068) and “Cátedra UAM-Telefónica”

Conditional-Sorting Local Binary Pattern Based on Gait Energy Image for Human Identification

[[abstract]]Gait recognition systems have recently attracted much interest from biometric researchers. This work proposes a new feature extraction method for gait representation and recognition. The new method is extended from the technique of Local Binary Pattern (LBP) by changing the sorting method of LBP according to the blend direction to create a new approach, Conditional-Sorting Local Binary Pattern (CS-LBP). After synchronizing and calibrating the gait sequence images, a cycle of images from the gait sequence can be captured to form a Gait Energy Image (GEI). We then apply the CS-LBP on GEI to derive different blend direction images and calculate the recognition ability for each blend direction image for feature selections. To solve the classification problem, the Euclidean distance and Nearest Neighbor (NN) approaches are used. With the experiments carried out on the CASIA-B gait database, our proposed gait representation has a very good recognition rate.[[sponsorship]]Worldcomp[[conferencetype]]國際[[conferencedate]]20130722~20130725[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Las Vegas, US

Gait Recognition based on Inverse Fast Fourier Transform Gaussian and Enhancement Histogram Oriented of Gradient

Gait recognition using the energy image representation of the average silhouette image in one complete cycle becomes a baseline in model-free approaches research. Nevertheless, gait is sensitive to any changes. Up to date in the area of feature extraction, image feature representation method based on the spatial gradient is still lacking in efficiency especially for the covariate case like carrying bag and wearing a coat. Although the use of Histogram of orientation Gradient (HOG) in pedestrian detection is the most effective method, its accuracy is still considered low after testing on covariate dataset. Thus this research proposed a combination of frequency and spatial features based on Inverse Fast Fourier Transform and Histogram of Oriented Gradient (IFFTG-HoG) for gait recognition. It consists of three phases, namely image processing phase, feature extraction phase in the production of a new image representation and the classification. The first phase comprises image binarization process and energy image generation using gait average image in one cycle. In the second phase, the IFFTG-HoG method is used as a features gait extraction after generating energy image. Here, the IFFTG-HoG method has also been improved by using Chebyshev distance to calculate the magnitude of the gradient to increase the rate of recognition accuracy. Lastly, K-Nearest Neighbour (k=NN) classifier with K=1 is employed for individual classification in the third phase. A total of 124 people from CASIA B dataset were tested using the proposed IFTG-HoG method. It performed better in gait individual classification as the value of average accuracy for the standard dataset 96.7%, 93.1% and 99.6%compared to HoG method by 94.1%, 85.9% and 96.2% in order. With similar motivation, we tested on Rempit datasets to recognize motorcycle rider anomaly event and our proposed method also outperforms Dalal Method