Face Detection and Recognition in a Hadoop Cluster
This research is focus on the implementation of High Performance and Distributive algorithm for solving the problem of detecting faces and to recognize people on a big bundle of images that then can be used on the implementation of security systems as closed-circuit television camera (CCTV), which c...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | México |
| Institución: | Instituto Tecnológico y de Estudios Superiores de Occidente |
| Repositorio: | Repositorio Institucional del ITESO |
| Idioma: | inglés |
| OAI Identifier: | oai:rei.iteso.mx:11117/5136 |
| Acceso en línea: | http://hdl.handle.net/11117/5136 |
| Access Level: | acceso abierto |
| Palabra clave: | Hadoop Cluster MapReduce OpenCV HIPI Face Detection Parallel Programming Distributed Systems |
| Sumario: | This research is focus on the implementation of High Performance and Distributive algorithm for solving the problem of detecting faces and to recognize people on a big bundle of images that then can be used on the implementation of security systems as closed-circuit television camera (CCTV), which could offer a quick solution for detecting unrecognized people on certain areas as an example. Another use of such algorithms could be the detection of certain objects like guns, cellphones, etc. in a big number of images. Face detection algorithm is implementing HAAR-LIKE methodology in a trained neuronal network as the first step, there are libraries like OpenCV that had already implemented this methodology which is been used as our computer vision library, on the other hand we have local binary pattern (LBP) which is used to trained the neural network for doing the face recognition task also included on OpenCV. To applied distributive techniques Hadoop Framework is taking an important role, been saved big number of images on its Hadoop Distributed File System (HDFS), for then becoming the data source to apply Map Reduce techniques to implement such algorithms on a distributive way, this dirty task is done by Yarn component which take the control of all servers on the cluster implemented “4 machines”. It is important to mentioned that University of Virginia has developed a library for image processing, Hadoop Image Processing Interface (HIPI) that allows to create images bundles and the easy interface to handle each image on the bundle. At the end, the result obtained is the number of faces found on the bundle and a Neural Network trained to identify people. |
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