| Abstract | dc.description.abstract | The aim of this work is to carry out a comparative study of face recognition methods that are suitable to work in unconstrained
environments. The analyzed methods are selected by considering their performance in former comparative studies, in addition to
be real-time, to require just one image per person, and to be fully online. In the study two local-matching methods, histograms
of LBP features and Gabor Jet descriptors, one holistic method, generalized PCA, and two image-matching methods, SIFTbased
and ERCF-based, are analyzed. The methods are compared using the FERET, LFW, UCHFaceHRI, and FRGC databases,
which allows evaluating them in real-world conditions that include variations in scale, pose, lighting, focus, resolution, facial
expression, accessories, makeup, occlusions, background and photographic quality. Main conclusions of this study are: there is
a large dependence of the methods on the amount of face and background information that is included in the face’s images,
and the performance of all methods decreases largely with outdoor-illumination. The analyzed methods are robust to inaccurate
alignment, face occlusions, and variations in expressions, to a large degree. LBP-based methods are an excellent election if we need
real-time operation as well as high recognition rates. | es_CL |
