Title: Cumulative foot pressure image recognition via Gabor filters and sparse representation classifier

Authors: Pedram Ahmad Khan Beigi; Aboozar Ghaffari

Addresses: Electrical Engineering Department, Ghiaseddin Jamshid Kashani University, Abyek, Qazvin, Iran ' Electrical Engineering Department, Iran University of Science and Technology, Tehran, Iran

Abstract: Analysis of cumulative foot pressure images (CFPIs) can be utilised like other biometrics for personal recognition. This biometric, along with other biometrics such as gait, can be helpful in identification. Also, this overcomes some drawbacks of common biometrics (such as face and gait) in data recording and taking people's foot data without knowing them. In the process of capturing cumulative foot pressure images, the spatial and temporal changes of ground reaction force during one gait cycle are recorded. In this biometric, there are some challenges such as walking at different speeds. In this paper, we present a new approach based on Gabor filters as a feature vector and sparse representation classification (SRC). To reduce the feature dimension, eigenfoot and linear discriminative analysis (LDA) are also used. To obtain translation and rotation invariant representation, a normalisation preprocess is applied. We evaluate the proposed approach via dataset D of the Chinese Academy of Science (CASIA) Gait-Footprint dataset containing the cumulative foot pressure images of 88 persons. The experimental results indicate that the proposed method has higher accuracy than other tested methods, and also is robust to changes in walking speed.

Keywords: cumulative foot pressure; Gabor filters; sparse representation classifier; eigenfoot; linear discriminative analysis; LDA.

DOI: 10.1504/IJBM.2023.129219

International Journal of Biometrics, 2023 Vol.15 No.2, pp.153 - 169

Received: 03 Dec 2020
Accepted: 07 Nov 2021
Published online: 01 Mar 2023 *

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