Structured Output SVM Prediction of Apparent Age, Gender and Smile From Deep Features
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00304551" target="_blank" >RIV/68407700:21230/16:00304551 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/CVPRW.2016.96" target="_blank" >http://dx.doi.org/10.1109/CVPRW.2016.96</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPRW.2016.96" target="_blank" >10.1109/CVPRW.2016.96</a>
Alternative languages
Result language
angličtina
Original language name
Structured Output SVM Prediction of Apparent Age, Gender and Smile From Deep Features
Original language description
We propose structured output SVM for predicting the apparent age as well as gender and smile from a single face image represented by deep features. We pose the problem of apparent age estimation as an instance of the multi-class structured output SVM classifier followed by a softmax expected value refinement. The gender and smile predictions are treated as binary classification problems. The proposed solution first detects the face in the image and then extracts deep features from the cropped image around the detected face. We use a convolutional neural network with VGG-16 architecture [25] for learning deep features. The network is pretrained on the ImageNet [24] database and then fine-tuned on IMDB-WIKI [21] and ChaLearn 2015 LAP datasets [8]. We validate our methods on the ChaLearn 2016 LAP dataset [9]. Our structured output SVMs are trained solely on ChaLearn 2016 LAP data. We achieve excellent results for both apparent age prediction and gender and smile classification.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
ISBN
978-1-5090-1437-8
ISSN
2160-7508
e-ISSN
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Number of pages
9
Pages from-to
730-738
Publisher name
IEEE
Place of publication
Piscataway (New Jersey)
Event location
Las Vegas
Event date
Jul 1, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000391572100089