Mr. Xuan Zhao Profile

Xuan Zhao

at Binghamton Univ

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Publications (2)

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Proceedings Article | 6 June 2024 Presentation + Paper

A homogeneous low-resolution face recognition method using correlation features at the edge

Xuan Zhao, Deeraj Nagothu, Yu Chen

Proceedings Volume 13062, 130620F (2024) https://doi.org/10.1117/12.3008368

KEYWORDS: Facial recognition systems, Feature extraction, Tunable filters, Image filtering, Performance modeling, Video surveillance, Machine learning, Education and training

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Face recognition technology has been well investigated in past decades and widely deployed in many real-world applications. However, low-resolution face recognition is still a challenging task in resource-constrained edge computing environment like the Internet of Video Things (IoVT) applications. For instance, low-resolution images are common in surveillance video streams, in which the rare information, variable angles, and light conditions create difficulties for recognition tasks. To address these problems, we optimized the correlation feature face recognition (CoFFaR) method and conducted experimental studies in two data preparation modes, symmetric and exhaustive arranging. The experimental results show that the CoFFaR method achieved an accuracy rate of over 82.56%, and the two-dimensional (2D) feature points after dimension reduction are uniformly distributed in a diagonal pattern. The analysis leads to the conclusion that the data augmentation advantage brought by the method of exhaustive arranging data preparation can effectively improve the performance, and the constraints by making the feature vector closer to its clustering center have no apparent improvement in the accuracy of the model identification.

Proceedings Article | 30 July 2019 Presentation + Paper

Face recognition in low-resolution surveillance video streams

Xuan Zhao, Yu Chen, Erik Blasch, Liwen Zhang, Genshe Chen

Proceedings Volume 11017, 110170J (2019) https://doi.org/10.1117/12.2518991

KEYWORDS: Facial recognition systems, Feature extraction, Video surveillance, Image resolution

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Face recognition technology has been widely adopted in many mission-critical applications as a means of human identification, controlled admission, E-bank authentication, and mobile device access. Security surveillance is also a growing application for face recognition techniques; however, challenges exist from low resolution (LR) and high noise, multi-angle and multi-distance changes, and different light conditions. In comparison, algorithms applied to cell phone imagery or other specific camera devices mainly function on high resolution images with fixed angles and small changes of illumination. As face recognition in security surveillance becomes more important in the era of dense urbanization, it is essential to develop algorithms that are able to provide satisfactory performance in processing the video frames generated by low resolution surveillance cameras. In this paper, we propose a novel face recognition method that is suitable for low resolution surveillance cameras. The technique is demonstrated on a face dataset generated from real-world surveillance scenarios, from which an end-to-end approach is taken to match high resolution (HR) images with low resolution (LR) images from the surveillance video. The experimental results validate the effectiveness of the novel method that improves the accuracy of face recognition in surveillance security scenarios.

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