Title: Collaborative representation with hole filling techniques for Kinect RGB-D face recognition

Authors: Aniketh Gaonkar; Narayan Vetrekar; Rajendra S. Gad

Addresses: School of Physical and Applied Sciences, Goa University, Taleigao-Plateau, Goa 403-206, India ' School of Physical and Applied Sciences, Goa University, Taleigao-Plateau, Goa 403-206, India ' School of Physical and Applied Sciences, Goa University, Taleigao-Plateau, Goa 403-206, India

Abstract: We present in this paper, three different filtering techniques based on kernel function to fill missing information in depth map images obtained from low resolution sensor such as Kinect to improve the performance accuracy of RGB-D face recognition systems. We propose in this study, an RGB-D face recognition scheme that combines depth map and colour image using wavelet average fusion followed by collaborative representation classifier (CRC) for comparison of reference and probe images. We present the evaluation results based on our GU-RGBD face database and IIIT-D face database to present the significance of our three different filters employed in hole filling. Further, our investigation presents an extensive experimental analysis using eight different feature extraction techniques independently across three different filters to demonstrate the potential of our proposed approach. The proposed approach of hole filling, improves the performance accuracy of RGB-D face recognition as compared to without employing filtering operations.

Keywords: face biometric; depth map; wavelet average fusion; feature extraction; collaborative representation classifier; CRC.

DOI: 10.1504/IJBM.2023.129223

International Journal of Biometrics, 2023 Vol.15 No.2, pp.170 - 193

Received: 02 Jun 2021
Accepted: 08 Jan 2022
Published online: 01 Mar 2023 *

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