Mobile APP for facial recognition
Abstract
Facial recognition technology allows individuals to be identified by analyzing facial characteristics that cannot be easily altered (the superficial arches, the areas around the cheekbones or the sides of the mouth). The technology is commonly used to compare real-time data shots of faces, even with movement, with stored templates, but it can also be used to compare static images such as those in passports. The facial identification process is basically divided into two tasks: detection and recognition. The first of them, detection, includes the location of one or more faces within an image, whether still or a video sequence. The second task, recognition, consists of comparing the face detected in the previous step with others previously stored in a database. These processes, detection and recognition, should not be totally independent because depending on the way in which a face is detected, it may be practically impossible to recognize it with faces from a database detected in a different way, hence recognition systems facial are strongly conditioned by the position and orientation of the subject's face with respect to the camera and the lighting conditions at the time of detection.
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References
(1) A. K. Jain, A. Ross and S. Prabhakar, "Biometrics: A Tool for Information Security", IEEE Transactions on information forensics and Security, Vol. 1, No. 2, pp. 125-143, June 2006.
(2) Anil K. Jain, Ajay Kumar, "Biometrics of Next generation: An overview", to appear in ‘Second Generation Biometrics’ Springer, 2010
(3) Gert M. Beumer, “Face recognition, a landmark tale”, PhD Thesis, October 16th 2009.
(4) Asker M. Bazer, Raymond N.J. Veldhuis, and Gerrie H. Croonen, “Likelihood Ratio-Based Detection of Facial Features”, pp. 323-329, 2003.
(5) G.M. Beumer, Q. Tao, A.M. Bazen, and R.N.J. Velhuis, “A landmark paper in face recognition”, 2006.
(6) Según el número de autores, adapte la zona correspondiente al nombre y filiación de manera oportuna. Intente no variar de manera notable el aspecto y tamaño de la zona.
(7) M. A. Turk and A. P. Pentland, “Face Recognition Using Eigenfaces”, Proc. IEEE,
(8) Laurenz Wiskott, Jean-Marc Felloues, Norbert Krüger, Christoph Von Der Malsburg, “Face recognition by Elastic Bunch Graph Matching”. IEEE Transactions on Pattern Analysis and Machine Intelligence,vol. 19, no. 7, pp. 775-779, Julio de 1997.
(9) Luis Carlos Molina, Lluís Belanche, and Àngela Nebot. Feature selection algorithms: Asurvey and experimental evaluation. In Proceedings of the 2002 IEEE International Conference on Data Mining, ICDM ’02, pages 306–, Washington, DC, USA, 2002. IEEE Computer Society.
(10) (Vieites, Enciclopedia de la Seguridad Informática. 2ª edición 2014)
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