Gait Recognition Robust to Speed Transition Using Mutual Subspace Method

Iwashita, Yumi; Sakano, Hitoshi; Kurazume, Ryo

doi:10.1007/978-3-319-23231-7_13

Cited by 10 publications

(13 citation statements)

References 10 publications

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“…In the second category, speed-invariant gait features are employed for gait recognition [11,12,14,16,23,39,40]. For example, in [16], based on Procrustes shape analysis descriptors, the differential composition model (DCM) was introduced to differentiate the effects on each body part caused by speed change.…”

Section: Speed-invariant Gait Recognition In the Within-walking Scenariomentioning

confidence: 99%

“…For example, in [16], based on Procrustes shape analysis descriptors, the differential composition model (DCM) was introduced to differentiate the effects on each body part caused by speed change. Iwashita et al [12] applied a mutual subspace method (MSM) to a set of gait silhouette images and a canonical angle between the gallery and probe subspaces is computed as dissimilarity measure, which is often chosen from the single-support phases in the cross-speed case. They further extended this approach in [11] by dividing the human body into multiple areas, and using a matching weight to select the relatively static parts.…”

Section: Speed-invariant Gait Recognition In the Within-walking Scenariomentioning

confidence: 99%

“…In this section, the proposed method is compared with additional state-of-the-art methods of speed-invariant gait recognition, i.e., the hidden Markov model (HMM)-based approach [23], stride normalization (SN) [41], speed transformation model (STM) [27], DCM [17], RSM [5], MSM [12], MSM-DA [11], and the state-of-the-art deep learningbased method, i.e., LB [45] using GEI on OUTD-A. For LB, we applied two strategies, i.e., separately fine-tuned different models for within-walking, within-running, and cross-mode cases (LB-sep), and fine-tuned a unified model using all the GEIs regardless of the gait mode variations (LB-uni).…”

Section: Outd-amentioning

confidence: 99%

“…It is clear that the SSGEIs can reduce the appearance differences caused by speed variance, posture change, and phase differences simultaneously Iwashita et al [12] applied a mutual subspace method (MSM) to cross-speed gait recognition, in which a set of silhouettes at various phases is represented by a subspace and a dissimilarity measure is obtained as the canonical angle between the subspaces for a matching pair. More specifically, in [12], a silhouette at an arbitrary phase, i.e., a transition from a single-support phase to double-support phase, is well approximated on each subspace and the canonical angle is obtained by minimizing the forming angle between a pair of silhouettes represented in each subspace. Therefore, in the cross-speed scenario, the canonical angle is expected to be obtained at the single-support phases, where the effect of the speed changes is minimal.…”

Section: Introductionmentioning

confidence: 99%

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Speed-Invariant Gait Recognition Using Single-Support Gait Energy Image

Makihara

et al. 2019

Multimed Tools Appl

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Gait is one of the most popular behavioral biometrics because it can be authenticated at a distance from a camera without subject cooperation. Speed differences between matching pairs, however, cause significant performance drops in gait recognition, and gait mode difference (i.e., walking versus running) makes gait recognition further challenging. We therefore propose a speed-invariant gait representation called single-support GEI (SSGEI), which realizes a good trade-off between speed invariance and stability by aggregating multiple frames around single-support phases. In addition, to mitigate the pose differences between walking and running modes at single-support phases, we morph walking and running SSGEIs into intermediate SSGEIs between walking and running mode, where we exploit a free-form deformation field from the walking or running modes to the intermediate mode obtained by training data. We finally apply Gabor filtering and spatial metric learning as postprocessing for further accuracy improvement. Experiments on two publicly available datasets, the OU-ISIR Treadmill Dataset A and the CASIA-C Dataset demonstrate that the proposed method yields the state-of-the-art accuracies in both identification and verification scenarios with a low computational cost.

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Section: Speed-invariant Gait Recognition In the Within-walking Scenariomentioning

confidence: 99%

Section: Speed-invariant Gait Recognition In the Within-walking Scenariomentioning

confidence: 99%

Section: Outd-amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Speed-Invariant Gait Recognition Using Single-Support Gait Energy Image

Makihara

et al. 2019

Multimed Tools Appl

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“…In a preliminary study we proposed to use a mutual subspace method (MSM) [9,10], which is an image set-based matching approach used for face recognition (a static/physiological biometric) to capture speed-invariant information [11]. This method involves dividing the human body area in multiple regions and use local movement information [12].…”

Section: Introductionmentioning

confidence: 99%

Speed invariant gait recognition—The enhanced mutual subspace method

et al. 2021

Self Cite

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This paper introduces an enhanced MSM (Mutual Subspace Method) methodology for gait recognition, to provide robustness to variations in walking speed. The enhanced MSM (eMSM) methodology expands and adapts the MSM, commonly used for face recognition, which is a static/physiological biometric, to gait recognition, which is a dynamic/behavioral biometrics. To address the loss of accuracy during calculation of the covariance matrix in the PCA step of MSM, we use a 2D PCA-based mutual subspace. Furhtermore, to enhance the discrimination capability, we rotate images over a number of angles, which enables us to extract richer gait features to then be fused by a boosting method. The eMSM methodology is evaluated on existing data sets which provide variable walking speed, i.e. CASIA-C and OU-ISIR gait databases, and it is shown to outperform state-of-the art methods. While the enhancement to MSM discussed in this paper uses combinations of 2D-PCA, rotation, boosting, other combinations of operations may also be advantageous.

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