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Multi-task Learning on the Edge for Effective Gender, Age, Ethnicity and Emotion Recognition by Pasquale Foggia; Antonio Greco; Alessia Saggese; Mario Vento is a Computer Science article available to read on EtoBox.

What is Multi-task Learning on the Edge for Effective Gender, Age, Ethnicity and Emotion Recognition about?

More and more real-world applications, especially in cognitive robotics, require the running of different computer vision algorithms in parallel on board of embedded devices with limited GPU and memory resources. Multi-task learning, namely the usage of the same model to perform multiple classification and/or regression tasks by learning a shared low level representation, revealed to be a valid solution to reduce computation and required memory space while preserving the accuracy. In this paper, we propose a solution for real-time user profiling based on a multi-task convolutional neural network (CNN) for gender, age, ethnicity and emotion recognition from face images. To find the best trade-off between accuracy and processing time, we evaluate three different architectures, specifically designed for the purpose, and backbones, based on MobileNet, ResNet and SENet, which include convolutional layers, residual blocks and attention modules that already demonstrated great potential in face analysis. We trained the multi-task neural network with a custom learning procedure, which solves the problems of missing labels, dataset imbalance and loss function imbalance through label masking,

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Author

Pasquale Foggia; Antonio Greco; Alessia Saggese; Mario Vento

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)