Human face classification by means of a local texture analysis using the CBIR technique and Points of Interest

2:25 pm - 2:50 pm 24 Thursday

Track

Pattern recognition

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.

References

[1] Gonzalo Pajares M. and Jesus M. de la Cruz G. Visión por computador imágenes digitales y aplicaciones. Alfa omega, 2da edición, Marzo 2008.

[2] J. F. Serrano-Talamantes, C. Aviles-Cruz, J. Villegas-Cortez, and J. H. Sossa-Azuela, Self organizing natural scene image retrieval, Expert Systems with Applications, 2012.

[3] K–A–Kim. Facial feature extraction using PCA and wavelet multi–resolution images. Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pages 17-19, Mayo 2004.

[4] H.Bay, A.Ess, T. Tuytelaars, and L.V–Goo. Surf: Speeded up robust features. Computer Vision and Image Understanding (CVIU), 110(3):346-359, 2008.

[5] Krystian Mikolajczyk and Cordelia Schmid. A performance evaluation of local descriptors. IEEE Trans.Pattern Anal. Mach. Intell., 27(10):1615-1630, October 2005.

[6] Javier Ruiz-del Solar, Rodrigo Verschae, and Mauricio Correa. Recognition of faces in unconstrained environments: A comparative study. EURASIP J. Adv. Signal Process, 2009:1:1-1:19, January 2009.

Speaker:
Juan Villegas