Robust Gait Recognition Against Speed Variation

Aqmar, Muhammad Rasyid; Sairyo, Koichi; Furui, Sadaoki

doi:10.1109/icpr.2010.536

Cited by 27 publications

(16 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We set K to 8 in our experiment for parameter pairs. Thus, parameter pairs were set to be (1,2), (2,4), (3,6), (4,8), (5,10), (6,12), (7,14), and (8,16). We tested those parameter pairs and selected parameter pair (2,4) since it performed best in our experiment.…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we propose a novel method of identifying humans from their gait under speed variations, where we combine cubic higher-order local auto-correlation (CHLAC) features and a statistical HMM framework [10]. We also employ the concatenation of CHLAC and gait silhouette-based principal component analysis (GaitSilhouette-PCA, or GSP) as features and combine them with HMM.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Gait-Based Person Identification against Walking Speed Variations

Aqmar

Sairyo

Furui

2012

IEICE Trans. Inf. & Syst.

Self Cite

View full text Add to dashboard Cite

SUMMARYVariations in walking speed have a strong impact on gaitbased person identification. We propose a method that is robust against walking-speed variations. It is based on a combination of cubic higherorder local auto-correlation (CHLAC), gait silhouette-based principal component analysis (GSP), and a statistical framework using hidden Markov models (HMMs). The CHLAC features capture the within-phase spatiotemporal characteristics of each individual, the GSP features retain more shape/phase information for better gait sequence alignment, and the HMMs classify the ID of each gait even when walking speed changes nonlinearly. We compared the performance of our method with other conventional methods using five different databases, SOTON, USF-NIST, CMU-MoBo, TokyoTech A and TokyoTech B. The proposed method was equal to or better than the others when the speed did not change greatly, and it was significantly better when the speed varied across and within a gait sequence.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Gait-Based Person Identification against Walking Speed Variations

Aqmar

Sairyo

Furui

2012

IEICE Trans. Inf. & Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The method using silhouette transformation based on walking speed (ST-WS) [36] separates static and dynamic features by fitting a human model and uses a factorization-based transformation model to transform the dynamic features from a reference speed to a target speed. The speed-invariant method in [37] uses the features extracted by Fisher discriminant analysis based cubic high-order local auto-correlation of the gait sequences to train a hidden Markov model. Different approaches have been used to address variations in carrying condition.…”

Section: Related Workmentioning

confidence: 99%

Gait recognition based on shape and motion analysis of silhouette contours

Das

Tjahjadi

2013

Computer Vision and Image Understanding

View full text Add to dashboard Cite

This paper presents a three-phase gait recognition method that analyses the spatio-temporal shape and dynamic motion (STS-DM) characteristics of a human subject's silhouettes to identify the subject in the presence of most of the challenging factors that affect existing gait recognition systems. In phase 1, phase-weighted magnitude spectra of the Fourier descriptor of the silhouette contours at ten phases of a gait period are used to analyse the spatio-temporal changes of the subject's shape. A component-based Fourier descriptor based on anatomical studies of human body is used to achieve robustness against shape variations caused by all common types of small carrying conditions with folded hands, at the subject's back and in upright position. In phase 2, a full-body shape and motion analysis is performed by fitting ellipses to contour segments of ten phases of a gait period and using a histogram matching with Bhattacharyya distance of parameters of the ellipses as dissimilarity scores. In phase 3, dynamic time warping is used to analyse the angular rotation pattern of the subject's leading knee with a consideration of arm-swing over a gait period to achieve identification that is invariant to walking speed, limited clothing variations, hair style changes and shadows under feet. The match scores generated in the three phases are fused using weight-based score-level fusion for robust identification in the presence of missing and distorted frames, and occlusion in the scene. Experimental analyses on various publicly available data sets show that STS-DM outperforms several state-of-the-art gait recognition methods.

show abstract

“…These model-based approach with dynamical features often led to the concept of model distance that depicts how far two models are [7]. On the contrary, static features usually led to statistical gait metrics which are easily implemented in popular classifiers like HMM, NN or SVM [9].…”

Section: Introductionmentioning

confidence: 99%

Velocity-Robust Gait Analysis for Human Identification through Constrained Learning of Stochastic Switched Auto-Regressive Model

Zhang

Inagaki

Suzuki

2013

SICE Journal of Control, Measurement, and System Integration

View full text Add to dashboard Cite

: Gait recognition is a promising non-intrusive biometric method. A robust and compact gait model is desirable in many security applications from public facilities to personal devices. Shape cues are chosen in most current researches except a few adopting dynamical features exclusively. And most of these systems are velocity-dependent. In order to explore more features of gait and to fit the varying environments of different applications, a new gait recognition model which synthesizes dynamic model and statistical one is designed. A kind of dynamical features, angular variables with respect to ankle joint, are adopted as the model's input. The proposed model has a circular structure consisted of 2 pairs of correlated states. A constrained learning algorithm is proposed under the model's special structure configured according to a 2-link virtual passive walking model which plays an important role both in the initialization and in the updating step. By evaluating the recognition rates of different models, the velocity-robust characteristics of the new model and its low computational load compared with conventional HMM are verified.

show abstract

Robust Gait Recognition Against Speed Variation

Cited by 27 publications

References 8 publications

Robust Gait-Based Person Identification against Walking Speed Variations

Robust Gait-Based Person Identification against Walking Speed Variations

Gait recognition based on shape and motion analysis of silhouette contours

Velocity-Robust Gait Analysis for Human Identification through Constrained Learning of Stochastic Switched Auto-Regressive Model

Contact Info

Product

Resources

About