Face detection on real low resolution surveillance videos

Rolando Jesus T. Cardenas, César A.Beltrán Castañón, Juan Carlos Gutierrez Cáceres

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4 Citas (Scopus)


The use of video cameras for security reasons has increased in recent times. Identify a person with automatic face detection systems have greater importance today; but the low-quality of the videos make it difficult and are still an open problem that many researchers are trying to solve. We propose a novel methodology for face detection on low-resolution videos based on parallel Gunnar Farnebäck optical flow algorithm, Haar Cascades and Local Binary Patterns. Our model does not use illumination normalization or super-resolution techniques, commonly used in literature. The results on the Caviar Database prove a better detection rate compared with OpenCv Library, Dlib C++ Library and Matlab function, which use the known Viola-Jones Haar cascade algorithm and HOGs. Even though these tools not have a number of detections up to 1%, our proposal can detect faces in a rate of 50%.

Idioma originalInglés
Título de la publicación alojadaICCDA 2018 - Proceedings of 2018 the 2nd International Conference on Compute and Data Analysis
EditorialAssociation for Computing Machinery
Número de páginas8
ISBN (versión digital)9781450363594
EstadoPublicada - 23 mar. 2018
Evento2nd International Conference on Compute and Data Analysis, ICCDA 2018 - DeKalb, Estados Unidos
Duración: 23 mar. 201825 mar. 2018

Serie de la publicación

NombreACM International Conference Proceeding Series


Conferencia2nd International Conference on Compute and Data Analysis, ICCDA 2018
País/TerritorioEstados Unidos


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