1 code implementation • CVPR 2021 • Alireza Sepas-Moghaddam, Fernando Pereira, Paulo Lobato Correia, Ali Etemad
We validate the performance of our proposed architecture in the context of two multi-perspective visual recognition tasks namely lip reading and face recognition.
no code implementations • 4 May 2021 • Pedro Albuquerque, Joao Machado, Tanmay Tulsidas Verlekar, Luis Ducla Soares, Paulo Lobato Correia
This paper presents a new dataset called GAIT-IT, captured from 21 subjects simulating 4 gait pathologies, with 2 severity levels, besides normal gait, being considerably larger than publicly available gait pathology datasets, allowing to train a deep learning model for gait pathology classification.
no code implementations • 10 Jan 2021 • Alireza Sepas-Moghaddam, Ali Etemad, Fernando Pereira, Paulo Lobato Correia
A subset of the in the wild dataset contains facial images with different expressions, annotated for usage in the context of face expression recognition tests.
no code implementations • 11 May 2019 • Alireza Sepas-Moghaddam, Ali Etemad, Fernando Pereira, Paulo Lobato Correia
In this context, this paper proposes two novel LSTM cell architectures that are able to jointly learn from multiple sequences simultaneously acquired, targeting to create richer and more effective models for recognition tasks.
no code implementations • 3 Jan 2019 • Alireza Sepas-Moghaddam, Fernando Pereira, Paulo Lobato Correia
In a world where security issues have been gaining growing importance, face recognition systems have attracted increasing attention in multiple application areas, ranging from forensics and surveillance to commerce and entertainment.
no code implementations • 25 May 2018 • Alireza Sepas-Moghaddam, Mohammad A. Haque, Paulo Lobato Correia, Kamal Nasrollahi, Thomas B. Moeslund, Fernando Pereira
This paper proposes a double-deep spatio-angular learning framework for light field based face recognition, which is able to learn both texture and angular dynamics in sequence using convolutional representations; this is a novel recognition framework that has never been proposed before for either face recognition or any other visual recognition task.