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Your search keyword '"Vidal, Flavio de Barros"' showing total 2 results
Start Over Author "Vidal, Flavio de Barros" Topic face
2 results on '"Vidal, Flavio de Barros"'

1. Estimating sex and age from a face: a forensic approach using machine learning based on photo-anthropometric indexes of the Brazilian population.

Author

Porto LF, Lima LNC, Franco A, Pianto D, Machado CEP, and Vidal FB

Subjects
Adolescent, Anatomic Landmarks, Anthropometry, Brazil, Child, Child, Preschool, Datasets as Topic, Female, Humans, Machine Learning, Male, Photography, Young Adult, Age Determination by Skeleton methods, Face physiology, Facial Bones growth & development, Forensic Anthropology methods, Sex Determination by Skeleton methods
Abstract

The facial analysis permits many investigations, some of the most important of which are craniofacial identification, facial recognition, and age and sex estimation. In forensics, photo-anthropometry describes the study of facial growth and allows the identification of patterns in facial skull development, for example, by using a group of cephalometric landmarks to estimate anthropological information. Previous works presented, as indirect applications, the use of photo-anthropometric measurements to estimate anthropological information such as age and sex. In several areas, automation of manual procedures has achieved advantages over and similar measurement confidence as a forensic expert. This manuscript presents an approach using photo-anthropometric indexes, generated from frontal faces cephalometric landmarks of the Brazilian population, to create an artificial neural network classifier that allows the estimation of anthropological information, in this specific case age and sex. This work is focused on four tasks: (i) sex estimation on ages from 5 to 22 years old, evaluating the interference of age on sex estimation; (ii) age estimation from photo-anthropometric indexes for four age intervals (1 year, 2 years, 4 years, and 5 years); (iii) age group estimation for thresholds of over 14 and over 18 years old; and; (iv) the provision of a new data set, available for academic purposes only, with a large and complete set of facial photo-anthropometric points marked and checked by forensic experts, measured from over 18,000 faces of individuals from Brazil over the last 4 years. The proposed binary classifier obtained significant results, using this new data set, for the sex estimation of individuals over 14 years old, achieving accuracy values higher than 0.85 by the F 1 measure. For age estimation, the accuracy results are 0.72 for the F 1 measure with an age interval of 5 years. For the age group estimation, the F 1 measures of accuracy are higher than 0.93 and 0.83 for thresholds of 14 and 18 years, respectively.

Published
2020
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2. A fast eye localization and verification method to improve face matching in surveillance videos.

Author

Oliveira, Lucas Sousa de, Borges, Dibio Leandro, Vidal, Flavio de Barros, and Chang, Lin-Ching

Abstract

Automatic face recognition (FR) problem has been extensively studied and applied to domains including biometrics, security, authentication, surveillance, and identification. Face recognition algorithms commonlly use high dimensional information and are therefore computationally expensive. The use of wrongly detected features can confuse the recognition process and make it even slower. This paper presents a set of heuristic lightweight features to describe eye regions. These features are used to further classify detected eye regions into false positive and positive ones. Note that the detected eye regions are all very similar, making it hard to find a good set of features to separate them. The classification is done by simply applying a threshold proportional to the variance of the data. The method was able to correctly classify 49.8% of the false positive samples, which if applied to Viola and Jones' best result could potentially turn its 93.7% performance into 96.8%. If the classification considers the pairs heuristics, the performance goes up to 83.3% at the expense of wrongly classifying positive samples. It would make the 93.7% become 98.9%. [ABSTRACT FROM PUBLISHER]

Published
2012
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