Evaluation of pseudorandom pixel placement in enhancing face recognition using VGG-Face

Muhammad Sabirin Hadis; Junichi Akita; Masashi Toda

doi:10.1117/12.3023756

14 March 2024 Evaluation of pseudorandom pixel placement in enhancing face recognition using VGG-Face

Muhammad Sabirin Hadis, Junichi Akita, Masashi Toda

Proceedings Volume 13074, Fifth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2023); 1307402 (2024) https://doi.org/10.1117/12.3023756
Event: Fifth International Conference on Image, Video Processing and Artificial Intelligence, 2023, Shenzhen, China

Abstract

In efforts to enhance face recognition performance, techniques ranging from super-resolution methods to the use of Local Binary Pattern (LBP) and deep learning have been explored. Among these, the pseudorandom pixel placement (PSE) technique has demonstrated potential in face recognition. Nevertheless, its testing was previously limited to just 8 subjects. This study undertakes a comprehensive evaluation of the PSE technique with a larger sample, utilizing 2000 subjects from the DigiFACE1M dataset and leveraging the state-of-the-art VGG-Face deep learning model. Through experiments involving 10 different PSE patterns on 144.000 face images, our findings indicate that, compared to Regular Pixel Placement (REG), PSE achieved an improvement in average accuracy by 1.05%, reduced the standard deviation by 1.47%, and resulted in 31 additional subjects achieving 100% accuracy. We conclude that PSE consistently outperforms REG in face recognition tasks using the VGG-Face model across the majority of tested scenarios.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Muhammad Sabirin Hadis, Junichi Akita, and Masashi Toda "Evaluation of pseudorandom pixel placement in enhancing face recognition using VGG-Face", Proc. SPIE 13074, Fifth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2023), 1307402 (14 March 2024); https://doi.org/10.1117/12.3023756

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KEYWORDS

Facial recognition systems

Deep learning

Image processing

Detection and tracking algorithms

RGB color model

CMOS sensors

Data modeling

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