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Face: face analysis for Commercial Entities
2010 IEEE International Conference on Image Processing, 2010Though face recognition gained significant attention and credibility in the last decade, quite few commercial applications were able to benefit from this. In this paper we propose FACE (Face analysis for Commercial Entities), a robust framework to address face analysis, aiming at supporting the activities of various Commercial Entities.
Maria De Marsico +2 more
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2017 30th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), 2017
With the global demand for extra security systems, and the growing of human-machine interaction, facial analysis in unconstrained environments (in the wild) became a hot-topic in recent computer vision research.Unconstrained environments include surveillance footage, social media photos and live broadcasts.This type of images and videos include no ...
Flávio H. de Bittencourt Zavan +6 more
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With the global demand for extra security systems, and the growing of human-machine interaction, facial analysis in unconstrained environments (in the wild) became a hot-topic in recent computer vision research.Unconstrained environments include surveillance footage, social media photos and live broadcasts.This type of images and videos include no ...
Flávio H. de Bittencourt Zavan +6 more
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Anthropometric Analysis of the Face
Journal of Craniofacial Surgery, 2016Facial anthropometric analysis is essential for planning cosmetic and reconstructive facial surgery, but has not been available in detail for modern Greeks. In this study, multiple measurements of the face were performed on young Greek males and females to provide a complete facial anthropometric profile of this population and to compare its facial ...
Georgios V, Zacharopoulos +5 more
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Detecting Arbitrarily Rotated Faces for Face Analysis
2019 IEEE International Conference on Image Processing (ICIP), 2019Current face detection concentrates on detecting tiny faces and severely occluded faces. Face analysis methods, however, require a good localization and would benefit greatly from some rotation information. We propose to predict a face direction vector (FDV), which provides the face size and orientation and can be learned by a common object detection ...
Frerk Saxen +4 more
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Face analysis through semantic face segmentation
Signal Processing: Image Communication, 2019Abstract Automatic face analysis, including head pose estimation, gender recognition, and expression classification, strongly benefits from an accurate segmentation of the human face. In this paper we present a multi-feature framework which first segments a face image into six parts, and then performs classification tasks on head pose, gender, and ...
Benini, Sergio +4 more
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Proceedings of the tenth ACM international conference on Multimedia - MULTIMEDIA '02, 2002
Future human-machine interaction interfaces will need a perfect understanding of the person's behavior so that machines can learn from it, react accordingly, synthetically reproduce this behavior afterwards, etc. The study of user's head action and face expression is fundamental to achieve this comprehension.
Ana C. Andrés del Valle +1 more
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Future human-machine interaction interfaces will need a perfect understanding of the person's behavior so that machines can learn from it, react accordingly, synthetically reproduce this behavior afterwards, etc. The study of user's head action and face expression is fundamental to achieve this comprehension.
Ana C. Andrés del Valle +1 more
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An analysis of 45 face presentations
American Journal of Obstetrics and Gynecology, 1951Abstract The incidence of face presentation is very small. Most textbooks state that its incidence ranges from 1 in 200 to 1 in 300. Hellman and associates recently found the incidence to be 1 in 356. On the other hand, other authors have reported incidences ranging between 1 in 529 and 1 in 576.
A C, POSNER, S, COHN
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Phantom faces for face analysis
Proceedings of International Conference on Image Processing, 1997The system presented is a specialized version of a general object recognition system. Images of faces are represented as graphs, labeled with topographical information and local features. New graphs of faces are generated by an elastic graph matching procedure comparing the new face with a set of stored model graphs: the face bunch graph. The result of
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