Facial Attributes Classification Using Multi-Task Representation Learning

Max Ehrlich, Timothy J. Shields, Timur Almaev, Mohamed R. Amer; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 47-55

Abstract


This paper presents a new approach for facial attribute classification using multi-task learning model. Our model learns a shared feature representation that is well-suited for multiple attribute classification. For learning this shared feature representation we use a Restricted Boltzmann Machines based model and enhance it with a factored multi-task component to become Multi-Task Restricted Boltzmann Machines. We operate on faces and facial landmark points and learn a joint feature representation for all attributes. We use an iterative learning approach consisting of a bottom-up/top-down pass to learn the shared representation of our multi-task model and at inference we use a bottom-up pass to predict the different tasks. Our approach is not restricted to any type of attributes, however, for this paper we focus only on facial attributes. We evaluate our approach on three publicly available datasets and show superior classification performance improvement over the state-of-the-art.

Related Material


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[bibtex]
@InProceedings{Ehrlich_2016_CVPR_Workshops,
author = {Ehrlich, Max and Shields, Timothy J. and Almaev, Timur and Amer, Mohamed R.},
title = {Facial Attributes Classification Using Multi-Task Representation Learning},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2016}
}