COMPUTER VISION FOR FACIAL RECOGNITION: ADVANCES AND RISKS

Resumen

This work presents a comprehensive analysis of computer vision applied to facial recognition, highlighting its technical advances and the risks associated with the use of this technology in contemporary contexts. The study aimed to explain the foundations of computer vision, describe the technical functioning of facial recognition algorithms, identify the main advances that have expanded the accuracy and dissemination of these systems, and discuss the ethical and social challenges arising from their growing use. The research was developed through a bibliographic review based on relevant works and studies in the fields of artificial intelligence, computer vision, deep learning, and technological ethics. The methodology made it possible to gather, interpret, and compare different theoretical and technical perspectives on the subject, enabling a solid and critical understanding of the phenomenon analyzed. The results showed that facial recognition has evolved rapidly due to the development of deep neural networks, increased computational capacity, and access to large image datasets. The accuracy of these models has increased significantly, leading to the implementation of the technology in mobile devices, access control systems, digital platforms, and security applications. However, the work identified important risks, such as violations of privacy, misuse of biometric data, mass surveillance, algorithmic biases that more intensely affect minority groups, and vulnerabilities associated with digital forgery techniques. The analysis concluded that, although facial recognition represents a significant advance within artificial intelligence, its use requires regulation, transparency, and responsible practices to prevent social harm and ensure the protection of individual rights. The study highlighted the need to balance technological innovation and ethics, pointing to pathways that favor the safe and conscientious application of this tool.