Abstract
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.
Highlights
Biometrics authentication has provided a large number of opportunities for security systems and forensic applications
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 to capture speed-invariant information [11]
In this paper we propose an enhanced MSM to improve accuracy and robustness to speed by the following contributing enhancements:
Summary
Biometrics authentication has provided a large number of opportunities for security systems and forensic applications. 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 suggests that MSM successfully extracts speedinvariant information, GEIs show speed-variant information as shown in Fig 2(b) and 2(d).
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