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SISTEMAS BIOMÉTRICOS:: ¿SEGURIDAD O PRIVACIDAD?
Abstract
Los sistemas biométricos son sistemas automáticos que reconocen patrones para establecer la identidad o autenticación de una persona basada en sus datos biométricos (Wayman, Jain, Maltoni & Malo, 2005). Este tipo de sistemas extrae un conjunto de características de los rasgos fisiológicos de un individuo y las compara con las características almacenadas en la base de datos y ejecuta una acción basada en el resultado de esta comparación (Jain, Flynn, Ross, 2010).
Este tipo de tecnología puede favorecer el aumento del nivel de seguridad, así como facilitar, abreviar y simplificar los procedimientos de identificación y de autenticación (AEPD, 2012). Por ello, muchas empresas han decidido utilizarlas para los controles de acceso de sus trabajadores o clientes. Sin embargo, esto ha generado varios debates sobre si vulnera los derechos de privacidad de estos individuos. El debate más reciente se ha generado por la implantación de un sistema de reconocimiento facial por parte de Mercadona, para detectar a personas con sentencias firmes o medidas cautelares que tengan una orden de alejamiento en contra del supermercado o alguno de sus trabajadores de cara a prohibirles la entrada a las tiendas (Rubio, 2020). Otras empresas como Amazon han prohibido la utilización de sus sistemas de reconocimiento facial a la policía estadounidense a raíz de las multitudinarias protestas en contra de la violencia policial (Vogel, 2020), así mismo IBM abandonó la tecnología de reconocimiento facial por las dudas éticas sobre su utilización (Mabanglo, 2020).
Todo esto genera dudas sobre cómo se está utilizando este tipo de tecnología, y en el caso de la implantación de estos sistemas surge una de las preguntas más debatida desde hace décadas, ¿es más importante la seguridad o la privacidad de los individuos?
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This paper studies the influence of demographics on the performance of face recognition algorithms. The recognition accuracies of six different face recognition algorithms (three commercial, two nontrainable, and one trainable) are computed on a large scale gallery that is partitioned so that each partition consists entirely of specific demographic cohorts. Eight total cohorts are isolated based on gender (male and female), race/ethnicity (Black, White, and Hispanic), and age group (18-30, 30-50, and 50-70 years old). Experimental results demonstrate that both commercial and the nontrainable algorithms consistently have lower matching accuracies on the same cohorts (females, Blacks, and age group 18-30) than the remaining cohorts within their demographic. Additional experiments investigate the impact of the demographic distribution in the training set on the performance of a trainable face recognition algorithm. We show that the matching accuracy for race/ethnicity and age cohorts can be improved by training exclusively on that specific cohort. Operationally, this leads to a scenario, called dynamic face matcher selection, where multiple face recognition algorithms (each trained on different demographic cohorts) are available for a biometric system operator to select based on the demographic information extracted from a probe image. This procedure should lead to improved face recognition accuracy in many intelligence and law enforcement face recognition scenarios. Finally, we show that an alternative to dynamic face matcher selection is to train face recognition algorithms on datasets that are evenly distributed across demographics, as this approach offers consistently high accuracy across all cohorts.
Biometric recognition, or simply biometrics, is the science of establishing the identity of a person based on physical or behavioral attributes. It is a rapidly evolving field with applications ranging from securely accessing ones computer to gaining entry into a country. While the deployment of large-scale biometric systems in both commercial and government applications has increased the public awareness of this technology, "Introduction to Biometrics" is the first textbook to introduce the fundamentals of Biometrics to undergraduate/graduate students. The three commonly used modalities in the biometrics field, namely, fingerprint, face, and iris are covered in detail in this book. Few other modalities like hand geometry, ear, and gait are also discussed briefly along with advanced topics such as multibiometric systems and security of biometric systems. Exercises for each chapter will be available on the book website to help students gain a better understanding of the topics and obtain practical experience in designing computer programs for biometric applications. These can be found at: http://www.csee.wvu.edu/~ross/BiometricsTextBook/.Designed for undergraduate and graduate students in computer science and electrical engineering, "Introduction to Biometrics" is also suitable for researchers and biometric and computer security professionals.
Is biometric identification a practical aid to ensuring individual and collective safety in an increasingly crowded and complicated world, or an Orwellian vision with the potential to strip us of our right to be let alone? In these early years of the 21st century, the sciences and technologies of surveillance and monitoring have advanced to the point where we now, collectively, have the capability to threaten the most basic democratic notions of individual autonomy and privacy. Yet at the same time, autonomous acts by unmonitored individuals and groups have cost the lives of thousands and have threatened our fundamental right to feel, and indeed be, safe and secure. This inherent conflict between collective safety and individual privacy rights needs to be addressed carefully by those who create and implement these technologies. Biometric identification technology in particular has raised concern among some civil libertarian groups for its potential to allow for government, and commercial, monitoring of activities of named individuals. We now have the technological means not just to monitor statistically the behavior of groups (which is the historic core of all social sciences), but to directly associate specific behavior with identified individuals. Although this capability, through fingerprinting, dates at least to the 19th century [1], it is the automatic association of actions to individuals through the use of measurement and computational machinery that some find so unsettling, particularly if such capability lies in the hands of government. Yet we fully expect our government to develop and utilize technologies to protect us from the actions of individuals with violent or otherwise criminal intent. What are the reasonable governmental uses of biometric identification technology under the limitations imposed by the US Constitution? In a democratic society which values government accountability and individual privacy, it is the rule of law which shapes the manner by which the government may interact fairly with its citizenry [2]. One of the basic principles shaping the rule of law in the USA is the right of individuals not to be treated unfairly by government. This broad notion manifests itself in two legal principles in particular: due process and individual privacy. The
Biometric Systems provides practitioners with an overview of the principles and methods needed to build reliable biometric systems. It covers three main topics: key biometric technologies, design and management issues, and the performance evaluation of biometric systems for personal verification/identification. The four most widely used technologies are focused on - speech, fingerprint, iris and face recognition. Key features include: in-depth coverage of the technical and practical obstacles which are often neglected by application developers and system integrators and which result in shortfalls between expected and actual performance; and protocols and benchmarks which will allow developers to compare performance and track system improvements.
Biometric authentication technologies are used for the machine identification of individuals. The human-generated patterns used may be primarily physiological or behavioral, but usually contain elements of both components. Examples include voice, handwriting, face, eye and fingerprint identification. In this paper, we look at these technologies and their applications in general, developing a systematic approach to classifying, analyzing and evaluating them. A general system model is shown and test results for a number of technologies are considered.
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