Alireza Sepas-Moghaddam

Queen's University | QueensU · Department of Electrical & Computer Engineering

Prior work has shown that the order in which different components of the face are learned using a sequential learner can play an important role in the performance of facial expression recognition systems. We propose FaceTopoNet, an end-to-end deep model for facial expression recognition, which is capable of learning an effective tree topology of th...

Gait recognition is an appealing biometric modality which aims to identify individuals based on the way they walk. Deep learning has reshaped the research landscape in this area since 2015 through the ability to automatically learn discriminative representations. Gait recognition methods based on deep learning now dominate the state-of-the-art in t...

Hand pose estimation (HPE) can be used for a variety of human-computer interaction applications such as gesture-based control for physical or virtual/augmented reality devices. Recent works have shown that videos or multi-view images carry rich information regarding the hand, allowing for the development of more robust HPE systems. In this paper, w...

Prior work has shown that the order in which different components of the face are learned using a sequential learner can play an important role in the performance of facial expression recognition systems. We propose FaceTopoNet, an end- to-end deep model for facial expression recognition, which is capable of learning an effective tree topology of t...

We propose an end-to-end architecture for facial expression recognition. Our model learns an optimal tree topology for facial landmarks, whose traversal generates a sequence from which we obtain an embedding to feed a sequential learner. The proposed architecture incorporates two main streams, one focusing on landmark positions to learn the structu...

Hand pose estimation (HPE) can be used for a variety of human-computer interaction applications such as gesture-based control for physical or virtual/augmented reality devices. Recent works have shown that videos or multi-view images carry rich information regarding the hand, allowing for the development of more robust HPE systems. In this paper, w...

We present a novel LSTM cell architecture capable of learning both intra- and inter-perspective relationships available in visual sequences captured from multiple perspectives. Our architecture adopts a novel recurrent joint learning strategy that uses additional gates and memories at the cell level. We demonstrate that by using the proposed cell t...

We present the Teacher-Student Generative Adversarial Network (TS-GAN) to generate depth images from a single RGB image in order to boost the recognition accuracy of face recognition (FR) systems. For our method to generalize well across unseen datasets, we design two components in the architecture, a teacher and a student. The teacher, which itsel...

Gait recognition is an appealing biometric modality which aims to identify individuals based on the way they walk. Deep learning has reshaped the research landscape in this area since 2015 through the ability to automatically learn discriminative representations. Gait recognition methods based on deep learning now dominate the state-of-the-art in t...

Light field (LF) cameras provide rich spatio-angular visual representations by sensing the visual scene from multiple perspectives and have recently emerged as a promising technology to boost the performance of human-machine systems such as biometrics and affective computing. Despite the significant success of LF representation for constrained faci...

Face representation learning solutions have recently achieved great success for various applications such as verification and identification. However, face recognition approaches that are based purely on RGB images rely solely on intensity information, and therefore are more sensitive to facial variations, notably pose, occlusions, and environmenta...

Light field (LF) cameras provide rich spatio-angular visual representations by sensing the visual scene from multiple perspectives and have recently emerged as a promising technology to boost the performance of human-machine systems such as biometrics and affective computing. Despite the significant success of LF representation for constrained faci...

Face representation learning solutions have recently achieved great success for various applications such as verification and identification. However, face recognition approaches that are based purely on RGB images rely solely on intensity information, and therefore are more sensitive to facial variations, notably pose, occlusions, and environmenta...

Long Short-Term Memory (LSTM) is a prominent recurrent neural network for extracting dependencies from sequential data such as time-series and multi-view data, having achieved impressive results for different visual recognition tasks. A conventional LSTM network, hereafter referred only as LSTM network, can learn a model to posteriorly extract info...

Gait recognition refers to the identification of individuals based on features acquired from their body movement during walking. Despite the recent advances in gait recognition with deep learning, variations in data acquisition and appearance, namely camera angles, subject pose, occlusions, and clothing, are challenging factors that need to be cons...

Gait recognition, referring to the identification of individuals based on the manner in which they walk, can be very challenging due to the variations in the viewpoint of the camera and the appearance of individuals. Current methods for gait recognition have been dominated by deep learning models, notably those based on partial feature representati...

Gait recognition refers to the identification of individuals based on features acquired from their body movement during walking. Despite the recent advances in gait recognition with deep learning, variations in data acquisition and appearance, namely camera angles, subject pose, occlusions, and clothing, are challenging factors that need to be cons...

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. To help to understand the landscape and abstraction levels relevant for face recognition systems, face recognition t...

A novel attention aware method is proposed to fuse two image modalities, RGB and depth, for enhanced RGB-D facial recognition. The proposed method uses two attention layers, the first focused on the fused feature maps generated by convolution layers, and the second focused on the spatial features of those maps. The training database is preprocessed...

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. This paper proposes a novel solution for classification of left/right hand movement by exploiting a L...

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. This paper proposes a novel solution for classification of left/right hand movement by exploiting a L...

Face recognition has attracted increasing attention due to its wide range of applications, but it is still challenging when facing large variations in the biometric data characteristics. Lenslet light field cameras have recently come into prominence to capture rich spatio-angular information, thus offering new possibilities for advanced biometric r...

With the emergence of lenslet light field cameras able to capture rich spatio-angular information from multiple directions, new frontiers in visual recognition performance have been opened. Since multiple 2D viewpoint images can be rendered from a light field, those multiple images, or descriptions extracted from them, can be organized as a pseudo-...

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. To help understanding the landscape and abstraction levels relevant for face recognition systems, face recognition t...

Face recognition has attracted increasing attention due to its wide range of applications but it is still challenging when facing large variations in the biometric data characteristics. Lenslet light field cameras have recently come into prominence to capture rich spatio-angular information, thus offering new possibilities for designing advanced bi...

Vulnerability of face recognition systems to presentation attacks has attracted increasing attention from the biometrics and forensics communities. Moreover, the recent availability of light field cameras is opening new possibilities for designing improved face presentation attack detection solutions. In this context, this paper provides the first...

Ear recognition is an emerging research area in image-based biometrics. The commercial availability of lenslet light field cameras able to capture full spatio-angular information has brought momentum to biometric and forensic research exploiting this new type of imaging sensors. This study is the first to consider the usage of light field cameras f...

Face recognition systems are becoming ubiquitous, but they are vulnerable to spoofing attacks. The recently available light field cameras can be used for spoofing attack detection. In this study, the IST Lenslet Light Field Face Spoofing Database (IST LLFFSD) is proposed, consisting of 100 genuine images, from 50 subjects, captured with a Lytro ILL...

Light field cameras are emerging as powerful devices to capture rich scene representations that provide unique advantages for analysis and representation purposes. Some recent works have shown the power and usefulness of the richer information carried out by light field imaging, notably for face recognition. However, it is still difficult to fully...

Swarm intelligence algorithms are amongst the most efficient approaches toward solving optimization problems. Up to now, most of swarm intelligence approaches have been proposed for optimization in static environments. However, numerous real-world problems are dynamic which could not be solved using static approaches. In this paper, a novel approac...

Clustering problems are considered amongst the prominent challenges in statistics and computational science. Clustering of nodes in wireless sensor networks which is used to prolong the life-time of networks is one of the difficult tasks of clustering procedure. In order to perform nodes’ clustering, a number of nodes are determined as cluster head...

Clustering problems are considered amongst the prominent challenges in statistics and computational science. Clustering of nodes in wireless sensor networks which is used to prolong the lifetime of networks is one of the difficult tasks of clustering procedure. In order to perform nodes' clustering, a number of nodes are determined as cluster heads...

Optimization is amongst the most significant problems in mathematics and sciences and many researchers are investigating different aspects of this problem. In this paper, a novel algorithm has been proposed for optimization in continuous static environments based on the individual and social behaviors of fish in their swarms. The proposed algorithm...

Swarm intelligence algorithms have been extensively used in clustering-based applications, e.g., image segmentation, which is one of the fundamental components in image analysis and pattern recognition domains. Particle swarm optimization (PSO) is among swarm intelligence algorithms that perform based on population and random search. In this paper,...

In spite of the positive role of colour features in pixel domain face recognition systems, previous recognition approaches in JPEG compressed domain have been proposed in grey space. In this study, for the first time, we investigated the effects of JPEG compressed colour features on the efficiency of the state-of-the-art approaches in this domain....

Artificial Fish Swarm Algorithm (AFSA) is one of the state-of-the-art swarm intelligence approaches that is widely used for optimization purposes. On the other hand, data clustering is an unsupervised classification technique which has been addressed by researchers in many disciplines and in many contexts. The contribution toward this study is twof...

JPEG compression standard is widely used for reducing the volume of images that are stored or transmitted via networks. In biometrics datasets, facial images are usually stored in JPEG compressed format and should be fully decompressed to be used in a face recognition system. Recently, in order to reduce the computational complexity of JPEG decompr...

In numerous real world optimization problems, objective function or constraints of the problem can be changed during time. If these undefined situations are occurred in optimization process, this problem is called dynamic. There are several challenges in dynamic environments optimization, so that algorithms designed for optimization in these enviro...

Swarm intelligence algorithms have been extensively used in clustering based applications e.g. image segmentation which is one of the fundamental components in image analysis and pattern recognition domains. Particle swarm optimization is amongst swarm intelligence algorithms that performs based on population and random search. In this paper, a hyb...

Objective function or the constraints and consequently the optimal value of the problem can be changed during time in Dynamic optimization problems. There are several challenges in dynamic environments, so that algorithms designed for optimization in these environments would utilize several mechanisms in order to conquer the challenges. In this pap...

Immunocytochemistry (ICC) is a microscopic imaging technique that is used to assess the presence of a specific antigen in cells utilizing a specific antibody for allowing visualization and examination processes. Number of cells in an ICC image is considered as one of the most important indicators in the examination process. In this paper, an image...

Immunocytochemistry is a common laboratory technique in which the quality of interaction between antibody antigen is evaluated on a cell surface. The amount of binding between antibodies and cancerous cells in Immunocytochemical images indicates how much the antibody is effective for treating the cancerous cell. In this paper, an automated system h...

Face recognition in JPEG compressed domain is one of the recent challenges in biometric systems, leading to a considerable reduction in computational overhead caused by decompression process, without any notable degradation in the recognition rates. In this paper, the potential of using a limited number of lowest frequency coefficients in JPEG comp...

Face recognition in JPEG compressed domain has turned into one of the important standpoints for reducing the computational overhead of decompression process without degradation in recognition accuracy. This domain needs some efficient methods for preselecting compressed coefficients and performing recognition process, which leads to improving the r...

Computational and space complexities and storage space are amongst the most important issues in designing face recognition systems. A common method for storing images in face recognition systems is compressing images using JPEG standard. Usually, the compressed images are fully decompressed for recognition, so that the recognition process is done i...

Calculating software complexity is one of the most challenging problems in the Software Engineering due to using them in estimating errors, having a landscape of software reliability, approximating costs of software implementation and maintenance, and delivering software with better quality. Most of the recent researches on calculating the software...