Gait recognition based on Kinect sensor

Ahmed, Mohammed Hussein; Al-Jawad, Naseer; Sabir, Azhin Tahir

doi:10.1117/12.2052588

Cited by 34 publications

(36 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 6 shows that our method achieves the best Method EER AUC MAP Ahmed [9] 0.2779 0.7811 0.7948 Andersson [10] 0.2858 0.7755 0.7996 Balazia 0.1674 0.8902 0.8907 Ball [11] 0.3949 0.6714 0.6931 Dikovski [13] 0.2273 0.8288 0.8287 Gavrilova [14] 0.1922 0.8521 0.8904 Jiang [15] 0.2393 0.8414 0.8497 Krzeszowski [16] 0.2287 0.8464 0.8593 Kumar [17] 0.3545 0.6528 0.6197 Kwolek [18] 0.1839 0.8796 0.8840 Preis [19] 0.4916 0.5225 0.5356 Sedmidubsky [20] 0.2823 0.7726 0.7558 Sinha [21] 0.2194 0.8289 0.8493 Table 4 EER, AUC and MAP derived from FAR/FRR, ROC and recall/precision, respectively, plotted in Figure 6.…”

Section: Results Of Comparative Evaluationmentioning

confidence: 99%

“…What follows is a to-date overview of gait recognition methods from MoCap data. Ahmed et al [9] present a method for gait recognition from horizontal and vertical distances of selected joint pairs. Temporally normalized within one gait cycle, the descriptors are classified by 1-NN queries with the CityBlock (Manhattan) distance.…”

Section: Related Methodsmentioning

confidence: 99%

“…The databases of all surveyed research groups are either acquired by Kinect tracking a small area [9,13,16,17,18,19] or contain pre-calibrated skeletons of all participants [20]. Another problem is their size: The largest structural gait database currently available has 230 gait cycles of 22 walking subjects.…”

Section: Experimental Databasesmentioning

confidence: 99%

See 2 more Smart Citations

Human gait recognition from motion capture data in signature poses

Balážia

Plataniotis

2016

IET biom.

View full text Add to dashboard Cite

Most contribution to the field of structure-based human gait recognition has been done through design of extraordinary gait features. Many research groups that address this topic introduce a unique combination of gait features, select a couple of well-known object classifiers, and test some variations of their methods on their custom Kinect databases. For a practical system, it is not necessary to invent an ideal gait feature -there have been many good geometric features designed -but to smartly process the data there are at our disposal. This work proposes a gait recognition method without design of novel gait features; instead, we suggest an effective and highly efficient way of processing known types of features. Our method extracts a couple of joint angles from two signature poses within a gait cycle to form a gait pattern descriptor, and classifies the query subject by the baseline 1-NN classifier. Not only are these poses distinctive enough, they also rarely accommodate motion irregularities that would result in confusion of identities. We experimentally demonstrate that our gait recognition method outperforms other relevant methods in terms of recognition rate and computational complexity. Evaluations were performed on an experimental database that precisely simulates street-level video surveillance environment.

show abstract

Section: Results Of Comparative Evaluationmentioning

confidence: 99%

Section: Related Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Human gait recognition from motion capture data in signature poses

Balážia

Plataniotis

2016

IET biom.

View full text Add to dashboard Cite

show abstract

“…• Ahmed by Ahmed et al [4] extracts the mean, standard deviation and skew during one gait cycle of horizontal distances (projected on the Z axis) between feet, knees, wrists and shoulders, and mean and standard deviation during one gait cycle of vertical distances (Y coordinates) of head, wrists, shoulders, knees and feet, and finally the mean area during one gait cycle of the triangle of root and two feet.…”

Section: Implementation Details Of Algorithmsmentioning

confidence: 99%

An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods

Balážia

Sojka

2017

Reproducible Research in Pattern Recognition

View full text Add to dashboard Cite

As a contribution to reproducible research, this paper presents a framework and a database to improve the development, evaluation and comparison of methods for gait recognition from motion capture (MoCap) data. The evaluation framework comprises source codes of state-of-the-art human-interpretable geometric features as well as our own approaches where gait features are learned by a modification of Fisher's Linear Discriminant Analysis with the Maximum Margin Criterion, and by a combination of Principal Component Analysis and Linear Discriminant Analysis. It includes a description and source codes of a mechanism for evaluating class separability coefficients of feature space and four classifier performance metrics. This framework also contains a tool for learning a custom classifier and for classifying a custom probe on a custom gallery. We provide an experimental database along with source codes for its extraction from the general CMU MoCap database.

show abstract

“…In several works such as [11,12] Vertical Distance Features (VDF) was developed by Ahmed et al in [11]. VDF describes the statistical characteristics of the absolute coordinate value changes of some joints when walking.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Relative distance features for gait recognition with Kinect

Yang

Dou

et al. 2016

Journal of Visual Communication and Image Representation

View full text Add to dashboard Cite

Gait and static body measurement are important biometric technologies for passive human recognition. Many previous works argue that recognition performance based completely on the gait feature is limited. The reason for this limited performance remains unclear. This study focuses on human recognition with gait feature obtained by Kinect and shows that gait feature can effectively distinguish from different human beings through a novel representation --relative distance-based gait features.Experimental results show that the recognition accuracy with relative distance features reaches up to 85%, which is comparable with that of anthropometric features. The combination of relative distance features and anthropometric features can provide an accuracy of more than 95%. Results indicate that the relative distance feature is quite effective and worthy of further study in more general scenarios (e.g., without Kinect).

show abstract

Gait recognition based on Kinect sensor

Cited by 34 publications

References 4 publications

Human gait recognition from motion capture data in signature poses

Human gait recognition from motion capture data in signature poses

An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods

Relative distance features for gait recognition with Kinect

Contact Info

Product

Resources

About