Abstract
Security is an essential condition for the well-being of human beings, which is why society seeks measures capable of preventing and identifying people who commit attacks against its security. One of the security solutions relied on video surveillance systems since through them scenes can be viewed directly and comfortably, through the Internet, using a computer, or any other mobile device. Nevertheless, this solution is not perfect because one of the most important limitations is related to the quality of the images acquired by these systems. They can be low resolution and can have changes in light intensity such as the clarity or darkness of the image, image perspective, and scale, which makes it difficult to recognize the people present in the room, even by human beings. Nowadays with the usage of Deep Learning algorithms, one can improve the quality of the images, which means, passing low to super-resolution images with relatively low effort. Nevertheless, the impact of this improvement is not analyzed to maintain the identity of the faces or person recognition task. This work proposes an artificial vision system for the improvement of faces in low-resolution images acquired through low-quality video surveillance systems. The work considers all the stages of a traditional artificial vision system, allowing the entry of images from a computer file, and improving the image through the super-resolution Enhance Deep Residual Networks algorithm, subsequently locates the person’s face using Mediapipe algorithm and performs the reconstruction only of the face area. Later, the impact of how good the performance of a face recognition model is taking advantage of the improvement image methods. We analyzed how much the enhancement procedure improves the overall face recognition system. We conduct our experiments with four image improvement algorithms: EDSR, ESPCN, FSRCNN, and LapSRN. Also, we select two standard datasets: 3DPeS and ChokePoint. Also, we did a final face recognition phase, where the LFW (Labeled Faces in the Wild) dataset was used to train and test a face recognition model. Related to image improvement, the results show that our proposal significantly visually improves the image more than quantitatively. Regarding the preservation of the face identity, the face recognition model achieves an accuracy of 94% by using images with enhancements and reconstructions in the facial area which is slightly lower than using the original images. Although considerable improvements were achieved with several super-resolution and enhancement models, it is crucial to analyze in which measure this improvement damages the face recognition model. Spite we achieved a very similar recognition rate comparing the improvement and not-improvement images, more analysis must be explored to go deeper into this conclusion.
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No datasets were generated or analysed during the current study.
Notes
Dlib C++ Library. http://dlib.net/
Face Detection - mediapipe. https://google.github.io/mediapipe
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Rebecca Zumaya: Writing—original draft, Conceptualization, Investigation, Visualization. Daniela Moctezuma: Writing—review & editing, Conceptualization. Andrea Magadán: Writing—review, Conceptualization.
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Zumaya, R., Moctezuma, D. & Magadán-Salazar, A. Improvement of low-quality images applied to intelligent video surveillance systems. SIViP 19, 38 (2025). https://doi.org/10.1007/s11760-024-03636-w
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DOI: https://doi.org/10.1007/s11760-024-03636-w