Search Results for author: Alireza Sepas-Moghaddam

Found 13 papers, 4 papers with code

Multi-Perspective LSTM for Joint Visual Representation Learning

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.

Face Recognition Lip Reading +1

Teacher-Student Adversarial Depth Hallucination to Improve Face Recognition

1 code implementation6 Apr 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.

Face Recognition

Deep Gait Recognition: A Survey

no code implementations18 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.

Gait Recognition

CapsField: Light Field-based Face and Expression Recognition in the Wild using Capsule Routing

no code implementations10 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.

Depth as Attention for Face Representation Learning

1 code implementation3 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).

Face Recognition Representation Learning

View-Invariant Gait Recognition with Attentive Recurrent Learning of Partial Representations

no code implementations18 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.

Gait Recognition

Gait Recognition using Multi-Scale Partial Representation Transformation with Capsules

no code implementations18 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.

Gait Recognition

Two-Level Attention-based Fusion Learning for RGB-D Face Recognition

1 code implementation29 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.

Face Recognition Transfer Learning

Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network

no code implementations6 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.

Deep Attention EEG +2

Long Short-Term Memory with Gate and State Level Fusion for Light Field-Based Face Recognition

no code implementations11 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.

Face Recognition Time Series

Face Recognition: A Novel Multi-Level Taxonomy based Survey

no code implementations3 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.

Face Recognition

A Double-Deep Spatio-Angular Learning Framework for Light Field based Face Recognition

no code implementations25 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.

Face Recognition

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