Gender Recognition Using Cognitive Modeling

Jens Fagertun; Tobias Andersen; Rasmus Reinhold Paulsen

doi:10.1007/978-3-642-33868-7_30

Gender Recognition Using Cognitive Modeling

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

In this work, we use cognitive modeling to estimate the ”gender strength” of frontal faces, a continuous class variable, superseding the traditional binary class labeling. To incorporate this continuous variable we suggest a novel linear gender classification algorithm, the Gender Strength Regression. In addition, we use the gender strength to construct a smaller but refined training set, by identifying and removing ill-defined training examples. We use this refined training set to improve the performance of known classification algorithms. Also the human performance of known data sets is reported, and surprisingly it seems to be quite a hard task for humans. Finally our results are reproduced on a data set of above 40,000 public Danish LinkedIN profile pictures.

Original language	English
Title of host publication	Computer Vision – ECCV 2012 : Workshops and Demonstrations, Part II
Publisher	Springer
Publication date	2012
Pages	300-308
ISBN (Print)	978-3-642-33867-0
ISBN (Electronic)	978-3-642-33868-7
DOIs	https://doi.org/10.1007/978-3-642-33868-7_30
Publication status	Published - 2012
Event	12th European Conference on Computer Vision (ECCV 2012) - Florence, Italy Duration: 7 Oct 2012 → 13 Oct 2012 http://eccv2012.unifi.it/

Conference

Conference	12th European Conference on Computer Vision (ECCV 2012)
Country/Territory	Italy
City	Florence
Period	07/10/2012 → 13/10/2012
Internet address	http://eccv2012.unifi.it/

Series	Lecture Notes in Computer Science
Volume	7584
ISSN	0302-9743

Keywords

Gender recognition
Linear Discriminant Analysis
Support Vector Machines
Cognitive Modeling
Linear Regression

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10.1007/978-3-642-33868-7_30

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@inproceedings{94435444c5da4d91aa40ff3a185bfcec,

title = "Gender Recognition Using Cognitive Modeling",

abstract = "In this work, we use cognitive modeling to estimate the ”gender strength” of frontal faces, a continuous class variable, superseding the traditional binary class labeling. To incorporate this continuous variable we suggest a novel linear gender classification algorithm, the Gender Strength Regression. In addition, we use the gender strength to construct a smaller but refined training set, by identifying and removing ill-defined training examples. We use this refined training set to improve the performance of known classification algorithms. Also the human performance of known data sets is reported, and surprisingly it seems to be quite a hard task for humans. Finally our results are reproduced on a data set of above 40,000 public Danish LinkedIN profile pictures.",

keywords = "Gender recognition, Linear Discriminant Analysis, Support Vector Machines, Cognitive Modeling, Linear Regression",

author = "Jens Fagertun and Tobias Andersen and Paulsen, \{Rasmus Reinhold\}",

year = "2012",

doi = "10.1007/978-3-642-33868-7\_30",

language = "English",

isbn = "978-3-642-33867-0",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "300--308",

booktitle = "Computer Vision – ECCV 2012",

note = "12th European Conference on Computer Vision (ECCV 2012) ; Conference date: 07-10-2012 Through 13-10-2012",

url = "http://eccv2012.unifi.it/",

}

TY - GEN

T1 - Gender Recognition Using Cognitive Modeling

AU - Fagertun, Jens

AU - Andersen, Tobias

AU - Paulsen, Rasmus Reinhold

PY - 2012

Y1 - 2012

N2 - In this work, we use cognitive modeling to estimate the ”gender strength” of frontal faces, a continuous class variable, superseding the traditional binary class labeling. To incorporate this continuous variable we suggest a novel linear gender classification algorithm, the Gender Strength Regression. In addition, we use the gender strength to construct a smaller but refined training set, by identifying and removing ill-defined training examples. We use this refined training set to improve the performance of known classification algorithms. Also the human performance of known data sets is reported, and surprisingly it seems to be quite a hard task for humans. Finally our results are reproduced on a data set of above 40,000 public Danish LinkedIN profile pictures.

AB - In this work, we use cognitive modeling to estimate the ”gender strength” of frontal faces, a continuous class variable, superseding the traditional binary class labeling. To incorporate this continuous variable we suggest a novel linear gender classification algorithm, the Gender Strength Regression. In addition, we use the gender strength to construct a smaller but refined training set, by identifying and removing ill-defined training examples. We use this refined training set to improve the performance of known classification algorithms. Also the human performance of known data sets is reported, and surprisingly it seems to be quite a hard task for humans. Finally our results are reproduced on a data set of above 40,000 public Danish LinkedIN profile pictures.

KW - Gender recognition

KW - Linear Discriminant Analysis

KW - Support Vector Machines

KW - Cognitive Modeling

KW - Linear Regression

U2 - 10.1007/978-3-642-33868-7_30

DO - 10.1007/978-3-642-33868-7_30

M3 - Article in proceedings

SN - 978-3-642-33867-0

T3 - Lecture Notes in Computer Science

SP - 300

EP - 308

BT - Computer Vision – ECCV 2012

PB - Springer

T2 - 12th European Conference on Computer Vision (ECCV 2012)

Y2 - 7 October 2012 through 13 October 2012

ER -

Gender Recognition Using Cognitive Modeling

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Conference

Keywords

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