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Machine Vision and Applications
Erschienen in: Machine Vision and Applications 2/2019

03.12.2018 | Original paper

Real-time demographic profiling from face imagery with Fisher vectors

verfasst von: Lorenzo Seidenari, Alessandro Rozza, Alberto Del Bimbo

Erschienen in: Machine Vision and Applications | Ausgabe 2/2019

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Abstract

In the last decade, demographic profiling from facial imagery has grown in its importance in the computer vision field. For demographic profiling, we usually mean gender, ethnicity, and age identification from face images. In this paper, we propose an efficient and effective profiling framework and we assess the quality of the proposed approach comparing the results obtained by our system with those achieved by other recently published methods on large datasets of facial images with different age, gender, and ethnicity. These results show how a carefully engineered pipeline of efficient image analysis and pattern recognition techniques leads to state-of-the-art results at 20 FPS using a single thread on a 1.6 GHZ i5-2467M processor.
Vorheriger Artikel Convolutional neural networks based on multi-scale additive merging layers for visual smoke recognition

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Fußnoten
1
“Ethnicity: definition of ethnicity”. Oxford Dictionaries. Oxford University Press. Retrieved 28 December 2013.
 
2
We call “white” a dataset of points sampled from a probability distribution with \({\varvec{\mu }}={\varvec{0}}\), and \({\varvec{\varSigma }}={\varvec{I}}\) where \({\varvec{I}}\) is the identity matrix.
 
3
d is a parameter to be set. Usually a good value is \(d \simeq min(log_2^2 N,D)\)
 
8
A video with showing the output of our algorithm on every subject in this dataset is available as supplementary material.
 
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Metadaten
Titel
Real-time demographic profiling from face imagery with Fisher vectors
verfasst von
Lorenzo Seidenari
Alessandro Rozza
Alberto Del Bimbo
Publikationsdatum
03.12.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Machine Vision and Applications / Ausgabe 2/2019
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-018-0991-2

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