The recognition of human faces represents an ongoing and very active area of research. This interest derives from the challenges posed by illumination occlusions and temporality. On the other hand, itsapplications continue to be very important, and more oriented toward security. During this lecture I am going to present a tested methodology for human faces classification on the basis of the analysis of the local texture of the face and contemplating the points of interest and the Content–Based Image Retrieval (CBIR) technique. The results achieved are excellent and the challenges lying ahead are of great interest, both for numerical floating point computing and Big Data applications.
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- Target tracking with template matching filtering
- Face recognition with correlation filters designed with multi-objective combinatorial optimization
- EEG signal implementation of Movement Intention for the Teleoperation of the Mobile Differential Robot
- LSA.Studio: Augmenting the LSA technique in pervasive environments
- Profiting from several recommendation algorithms using a scalable approach
- Juan Villegas