Detección de ataques de presentación facial basado en siamese-LSTM y el análisis del flujo óptico y puntos de referencia facial

Arnold Jair Jimenez Vargas; Rubiel Vargas Cañas; Carlos Alberto Cobos Lozada; Humberto Loaiza Correa

doi:10.24054/rcta.v1i43.2888

Autores/as

Arnold Jair Jimenez Vargas Universidad del Cauca https://orcid.org/0009-0003-8314-2062
Rubiel Vargas Cañas Universidad del Cauca https://orcid.org/0000-0003-1548-942X
Carlos Alberto Cobos Lozada Universidad del Cauca https://orcid.org/0000-0002-6263-1911
Humberto Loaiza Correa Universidad del Valle https://orcid.org/0000-0001-7206-7333

DOI:

https://doi.org/10.24054/rcta.v1i43.2888

Palabras clave:

Biometría, contra la suplantación de identidad, red neuronal siamese, red LSTM, flujo óptico, puntos de referencia faciales

Resumen

La autenticación por medio de la biometría facial se ha vuelto fundamental para verificar la identidad de las personas en transacciones en línea, ya que mecanismos clásicos como la autenticación por nombre de usuario y contraseña han demostrado ser poco fiables, ya que los usuarios suelen escoger contraseñas que son fáciles de recordar. Sin embargo, el avance en la fabricación de modelos con materiales como el látex, el aumento en la calidad de las impresiones y la mejora en las resoluciones de las pantallas han exigido que los sistemas de detección de fraude se adapten rápidamente a las nuevas condiciones. El presente trabajo muestra una propuesta para abordar el problema de la detección de ataques de presentación por medio de la extracción del flujo óptico y los puntos de referencia facial y su análisis por medio de una red siamese. Para evaluar el modelo propuesto, se utilizaron tres data sets: Rose-youtu, Replay-attack y Replay-mobile, y las métricas HTER y EER.

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