Deep multi-task learning for gait-based biometrics

The task of identifying people by the way they walk is known as 'gait recognition'. Although gait is mainly used for identification, additional tasks as gender recognition or age estimation may be addressed based on gait as well. In such cases, traditional approaches consider those tasks a...

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Bibliographic Details
Main Authors: Marin-Jimenez, M.J., Castro, F.M., Guil, N., de la Torre, F., Medina-Carnicer, R.
Format: Conference Proceeding
Language:English
Subjects:
Biometrics
Convergence
Deep Neural Networks
Estimation
Gait recognition
Measurement
Multi-task learning
Task analysis
Training
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Summary:The task of identifying people by the way they walk is known as 'gait recognition'. Although gait is mainly used for identification, additional tasks as gender recognition or age estimation may be addressed based on gait as well. In such cases, traditional approaches consider those tasks as independent ones, defining separated task-specific features and models for them. This paper shows that by training jointly more than one gait-based tasks, the identification task converges faster than when it is trained independently, and the recognition performance of multi-task models is equal or superior to more complex single-task ones. Our model is a multi-task CNN that receives as input a fixed-length sequence of optical flow channels and outputs several biometric features (identity, gender and age).
ISSN:2381-8549
DOI:10.1109/ICIP.2017.8296252