no code implementations • 30 May 2015 • Reza Azizi, Hasan Sedghi, Hamid Shoja, Alireza Sepas-Moghaddam
In this study, a novel algorithm is proposed based on Artificial Fish Swarm Algorithm (AFSA) for clustering procedure.
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.
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 • 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 • 6 Aug 2019 • Guangyi Zhang, Vandad Davoodnia, Alireza Sepas-Moghaddam, Yaoxue Zhang, Ali Etemad
Classifying limb movements using brain activity is an important task in Brain-computer Interfaces (BCI) that has been successfully used in multiple application domains, ranging from human-computer interaction to medical and biomedical applications.
1 code implementation • 29 Feb 2020 • Hardik Uppal, Alireza Sepas-Moghaddam, Michael Greenspan, Ali Etemad
A novel attention aware method is proposed to fuse two image modalities, RGB and depth, for enhanced RGB-D facial recognition.
no code implementations • 18 Oct 2020 • Alireza Sepas-Moghaddam, Ali Etemad
Our proposed model has been extensively tested on two large-scale CASIA-B and OU-MVLP gait datasets using four different test protocols and has been compared to a number of state-of-the-art and baseline solutions.
no code implementations • 18 Oct 2020 • Alireza Sepas-Moghaddam, Saeed Ghorbani, Nikolaus F. Troje, Ali Etemad
In this context, we propose a novel deep network, learning to transfer multi-scale partial gait representations using capsules to obtain more discriminative gait features.
1 code implementation • 3 Jan 2021 • Hardik Uppal, Alireza Sepas-Moghaddam, Michael Greenspan, Ali Etemad
Our novel attention mechanism directs the deep network "where to look" for visual features in the RGB image by focusing the attention of the network using depth features extracted by a Convolution Neural Network (CNN).
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 • 18 Feb 2021 • Alireza Sepas-Moghaddam, Ali Etemad
Gait recognition is an appealing biometric modality which aims to identify individuals based on the way they walk.
1 code implementation • ICCV 2021 • Hardik Uppal, Alireza Sepas-Moghaddam, Michael Greenspan, Ali Etemad
Moreover, face recognition experiments demonstrate that our hallucinated depth along with the input RGB images boosts performance across various architectures when compared to a single RGB modality by average values of +1. 2%, +2. 6%, and +2. 6% for IIIT-D, EURECOM, and LFW datasets respectively.
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 • 5 Dec 2021 • Mojtaba Kolahdouzi, Alireza Sepas-Moghaddam, Ali Etemad
We propose an end-to-end architecture for facial expression recognition.
Facial Expression Recognition Facial Expression Recognition (FER)
no code implementations • 13 Sep 2022 • Mojtaba Kolahdouzi, Alireza Sepas-Moghaddam, Ali Etemad
We perform extensive experiments on four large-scale in-the-wild facial expression datasets - namely AffectNet, FER2013, ExpW, and RAF-DB - and one lab-controlled dataset (CK+) to evaluate our approach.
Facial Expression Recognition Facial Expression Recognition (FER)