Face detection base on fuzzy skin region segmentation

Nasrabadi, Abbas; Haddadnia, Javad

doi:10.1109/icetc.2010.5530048

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…The most interesting findings, in the context of supervised learning for ISSN 2007-9737 face recognition [14], are that the support vector machine performs better than other classifiers and that the resolution of a face does not affect the classification rate [15]. Other works [16,17,18,19,20] use vector color models to separate RGB images in order to perform skin color detection. All these methods have been useful and they work very well under controlled environments.…”

Section: Introductionmentioning

confidence: 99%

Facial Geometry Identification through Fuzzy Patterns with RGBD Sensor

Fernandez-Cervantes

Becerril-García

Ramos

et al. 2015

CyS

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Automatic human facial recognition is an important and complicated task; it is necessary to design algorithms capable of recognizing the constant patterns in the face and to use computing resources efficiently. In this paper we present a novel algorithm to recognize the human face in real time; the system's input is the depth and color data from the Microsoft KinectTM device. The algorithm recognizes patterns/shapes on the point cloud topography. The template of the face is based in facial geometry; the forensic theory classifies the human face with respect to constant patterns: cephalometric points, lines, and areas of the face. The topography, relative position, and symmetry are directly related to the craniometric points. The similarity between a point cloud cluster and a pattern description is measured by a fuzzy pattern theory algorithm. The face identification is composed by two phases: the first phase calculates the face pattern hypothesis of the facial points, configures each point shape, the related location in the areas, and lines of the face. Then, in the second phase, the algorithm performs a search on these face point configurations.

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