momina moetesum - Academia.edu (original) (raw)

Papers by momina moetesum

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)

Graphomotor impressions are a product of complex cognitive, perceptual and motor skills and are w... more Graphomotor impressions are a product of complex cognitive, perceptual and motor skills and are widely used as psychometric tools for the diagnosis of a variety of neuro-psychological disorders. Apparent deformations in these responses are quantified as errors and are used are indicators of various conditions. Contrary to conventional assessment methods where manual analysis of impressions is carried out by trained clinicians, an automated scoring system is marked by several challenges. Prior to analysis, such computerized systems need to extract and recognize individual shapes drawn by subjects on a sheet of paper as an important pre-processing step. The aim of this study is to apply deep learning methods to recognize visual structures of interest produced by subjects. Experiments on figures of Bender Gestalt Test (BGT), a screening test for visuo-spatial and visuo-constructive disorders, produced by 120 subjects, demonstrate that deep feature representation brings significant improvements over classical approaches. The study is intended to be extended to discriminate coherent visual structures between produced figures and expected prototypes.

Neural Computing and Applications

To date, Artificial Intelligence systems for handwriting and drawing analysis have primarily targ... more To date, Artificial Intelligence systems for handwriting and drawing analysis have primarily targeted domains such as writer identification and sketch recognition. Conversely, the automatic characterization of graphomotor patterns as biomarkers of brain health is a relatively less explored research area. Despite its importance, the work done in this direction is limited and sporadic. This paper aims to provide a survey of related work to provide guidance to novice researchers and highlight relevant study contributions. The literature has been grouped into “visual analysis techniques” and “procedural analysis techniques”. Visual analysis techniques evaluate offline samples of a graphomotor response after completion. On the other hand, procedural analysis techniques focus on the dynamic processes involved in producing a graphomotor reaction. Since the primary goal of both families of strategies is to represent domain knowledge effectively, the paper also outlines the commonly employed...

Tenth International Conference on Graphics and Image Processing (ICGIP 2018)

Segmentation of constituent shapes is a vital yet challenging step in any automated sketch analys... more Segmentation of constituent shapes is a vital yet challenging step in any automated sketch analysis and interpretation system. Conventional stroke level sketch segmentation techniques perform well on a single shape, nevertheless, their performance degrades in a cluttered multi-object sample. On the contrary, object level techniques rely on proximity based perceptual grouping for shapes with disjoint constituent parts, which requires high level semantic knowledge. To overcome these challenges, we propose the use of state-of-the-art convolutional object detectors for the detection and segmentation of hand drawn shapes from offline samples of a neuropsychological drawing test i.e. Bender Gestalt Test (BGT). Experiments with different combinations of convolutional meta-architectures and feature extractors show that such networks can successfully be employed for sketch segmentation purposes even with limited training data and resources. Amongst all network combinations under evaluation in this study, Faster R-CNNs with ResNet-101 as feature extractor, outperform others by achieving precision, recall and F-measure values of 92.93%, 95.24% and 94.07% respectively.

2017 13th International Conference on Emerging Technologies (ICET), 2017

Smart phones with high resolution cameras have enabled innovative computer vision applications th... more Smart phones with high resolution cameras have enabled innovative computer vision applications that apply techniques like real-time image processing and virtual reality to provide a close approximation of reality. In this paper, we present an Android application that allows users to play a virtual piano by using a keyboard drawn on a piece of paper. The application allows the user to point the camera of a hand held device towards the keyboard, and process image of the paper keyboard in real time. The application then detects fingers placed on a key and after key detection plays the corresponding sounds. Initial results demonstrate the potential of such an application in providing a viable replacement for heavy and expensive instruments.

2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2020

Parkinson's disease (PD) is commonly characterized by several motor impairments like tremor, ... more Parkinson's disease (PD) is commonly characterized by several motor impairments like tremor, muscular rigidity and bradykinesia, that are collectively termed as ‘Parkinson's disease dysgraphia’. In an attempt to identify these motor-based Parkinsonian symptoms, experts have persistently been evaluating various dynamic attributes of handwriting, like pen pressure/position, stroke speed/trajectory, and on-surface/in-air time taken, captured with the help of online acquisition tools. Such devices not only capture various aspects of handwriting but provide rich sequential information that can be utilized to identify unique patterns from handwriting samples of PD patients. In this paper, we propose a model based on Bidirectional Gated Recurrent Units (BiGRU) to assess the potential of handwriting-based sequential information in the identification of Parkinsonian symptoms. One-dimensional convolution is applied to raw sequences and the resulting feature sequences are employed to train the BiGRU model for prediction. The results of our experiments validate the potential of our proposed technique in comparison to the state-of-the-art.

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), 2019

Source printer identification for printed document forgery detection has gained much significance... more Source printer identification for printed document forgery detection has gained much significance in recent years. Traditional techniques employed in the literature are highly text dependent and therefore may prove insufficient in certain scenarios. In this study, we present a text-independent approach for effective characterization of source printer, using deep visual features. By employing transfer learning on pre-trained Convolutional Neural Networks (CNNs), we achieve significant recognition results on a dataset of 1200 documents from 20 different printers (13 laser and 7 inkjet). A comparison with various conventional features on the same dataset demonstrate that our proposed methodology classifies printed documents more accurately and effectively.

2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2018

Expert Systems with Applications, 2021

Parkinson's disease (PD) is commonly characterized by several motor symptoms, such as bradykinesi... more Parkinson's disease (PD) is commonly characterized by several motor symptoms, such as bradykinesia, akinesia, rigidity, and tremor. The analysis of patients' fine motor control, particularly handwriting, is a powerful tool to support PD assessment. Over the years, various dynamic attributes of handwriting, such as pen pressure, stroke speed, in-air time, etc., which can be captured with the help of online handwriting acquisition tools, have been evaluated for the identification of PD. Motion events, and their associated spatio-temporal properties captured in online handwriting, enable effective classification of PD patients through the identification of unique sequential patterns. This paper proposes a novel classification model based on onedimensional convolutions and Bidirectional Gated Recurrent Units (BiGRUs) to assess the potential of sequential information of handwriting in identifying Parkinsonian symptoms. One-dimensional convolutions are applied to raw sequences as well as derived features; the resulting sequences are then fed to BiGRU layers to achieve the final classification. The proposed method outperformed state-of-the-art approaches on the PaHaW dataset and achieved competitive results on the NewHandPD dataset.

Document Analysis and Recognition – ICDAR 2021 Workshops, 2021

2018 International Conference on Frontiers of Information Technology (FIT), 2018

Historical manuscript dating is a challenging task that requires extensive domain knowledge regar... more Historical manuscript dating is a challenging task that requires extensive domain knowledge regarding varying scripts, writing styles and instruments involved. Increased digitization of historical documents for preservation and analysis purposes have encouraged the relevant community to design techniques for computerized dating of historical manuscripts. This paper presents a performance based comparative analysis of popular textural features for document dating purposes. Outcomes of a comprehensive series of experiments demonstrate that a combined textural features-based approach is more effective than employing individual texture features. A 345-dimensional feature vector is extracted by using a combination of individual features from Gabor filters, Uniform Local Binary Patterns and Histogram of Local Binary Patterns, whichisthenfedtoaLinear Discriminant Analysis (LDA) classifier. Performance evaluation of our proposed scheme on the Medieval Paleographical Scale (MPS) dataset gives a reduced Mean Absolute Error of 20.13.

2019 International Conference on Document Analysis and Recognition (ICDAR), 2019

Digitization of historical manuscripts from premodern eras, has captivated the document analysis ... more Digitization of historical manuscripts from premodern eras, has captivated the document analysis and pattern recognition community in recent years. Estimation of the period of production of such documents is a challenging yet favored research problem. In this paper, we present a deep learning based approach to effectively characterize the year of production of sample documents from the Medieval Paleographical Scale (MPS) dataset. By employing transfer learning on a number of popular pre-trained Convolutional Neural Network (CNN) models, we have significantly reduced the Mean Absolute Error (MAE) reported in previous studies.

2017 International Conference on Frontiers of Information Technology (FIT), 2017

Drawing tests have been long used by practitioners for early screening of a number of psychologic... more Drawing tests have been long used by practitioners for early screening of a number of psychological and neurological impairments. These brain functioning tests are used by psychologists to understand feelings, personality and reactions of individuals to different circumstances. Among these, Human Figure Drawing Test (HFDT) is a popular instrument for the assessment of cognitive functioning of individuals. While the HFDT has various dimensions, the focus of this study lies on the face of the drawn figure. A computerized system that analyzes the hand-drawn facial images to extract the expressions from the image is proposed. Sketch of human face is drawn by the subject and then fed to the system, the image is then binarized and segmented into different facial components. Features (based on local binary patterns, gray level co-occurrence matrices and histogram of oriented gradients) computed from the facial components are used to train an SVM classifier to learn to distinguish between four expression classes, ‘happy’, ‘sad’, ‘angry’ and ‘neutral’. The system evaluated on a custom developed database of sketches realized promising results. The developed system could serve as a useful module toward development of a complete automated system to score human figure drawing test.

Pattern Recognition Letters, 2018

Parkinson's disease (PD) is a degenerative disorder that progressively affects the central nervou... more Parkinson's disease (PD) is a degenerative disorder that progressively affects the central nervous system causing muscle rigidity, tremors, slowed movements and impaired balance. Sophisticated diagnostic procedures like SPECT scans can detect changes in the brain caused by PD but are only effective once the disease has advanced considerably. Analysis of subtle variations in handwriting and speech can serve as potential tools for early prediction of the disease. While traditional techniques mostly rely on dynamic (kinematic and spatio-temporal) features of handwriting, in this study, we quantitatively evaluate the visual attributes in characterization of graphomotor samples of PD patients. For this purpose, Convolutional Neural Networks are employed to extract discriminating visual features from multiple representations of various graphomotor samples produced by both control and PD subjects. The extracted features are then fed to a Support Vector Machine (SVM) classifier. Evaluations are carried out on a dataset of 72 subjects using early and late fusion techniques and an overall accuracy of 83% is realized with solely visual information.

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2016

Prediction of gender and other demographic attributes of individuals from handwriting samples off... more Prediction of gender and other demographic attributes of individuals from handwriting samples offers an interesting basic, as well as applied research problem. The correlation between gender and the visual appearance of handwriting has been validated by a number of studies and the present study is based on the same idea. We exploit the textural measurements as the discriminating attribute between male and female writings. The textural information in a writing is captured by applying a bank of Gabor filters to the image of handwriting. The mean and standard deviation values of the filter responses are collected in matrix and the Fourier transform of the matrix is used as a feature. Classification is carried out using a feed forward neural network. The proposed technique evaluated on a subset of the QUWI database realized promising results under different experimental settings.

2015 13th International Conference on Document Analysis and Recognition (ICDAR), 2015

Drawing tests have been long used by practitioners and researchers for early detection of psychol... more Drawing tests have been long used by practitioners and researchers for early detection of psychological and neurological impairments. These tests allow subjects to naturally express themselves as opposed to an interview or a written assessment. Bender Gestalt Test (BGT) is a well-known and established neurological test designed to detect signs of perceptual distortions. Subjects are shown a number of geometric patterns for reconstruction and assessments are made by observing properties like rotation, angulations, simplification and closure difficulty. The manual scoring of the test, however, is a time consuming and lengthy procedure especially when a large number of subjects is to be analyzed. This paper proposes the application of image analysis techniques to automatically score a subset of hand drawn images in the BGT test. A comparison of the scores reported by the automated system with those assigned by the psychologists not only reveals the effectiveness of the proposed system but also reflects the huge research potential this area possesses.

Document Analysis and Recognition – ICDAR 2021, 2021

Human-Robot Interaction - Theory and Application, Jul 4, 2018

Long-term companionship, emotional attachment and realistic interaction with robots have always b... more Long-term companionship, emotional attachment and realistic interaction with robots have always been the ultimate sign of technological advancement projected by sci-fi literature and entertainment industry. With the advent of artificial intelligence, we have indeed stepped into an era of socially believable robots or humanoids. Affective computing has enabled the deployment of emotional or social robots to a certain level in social settings like informatics, customer services and health care. Nevertheless, social believability of a robot is communicated through its physical embodiment and natural expressiveness. With each passing year, innovations in chemical and mechanical engineering have facilitated lifelike embodiments of robotics; however, still much work is required for developing a "social intelligence" in a robot in order to maintain the illusion of dealing with a real human being. This chapter is a collection of research studies on the modeling of complex autonomous systems. It will further shed light on how different social settings require different levels of social intelligence and what are the implications of integrating a socially and emotionally believable machine in a society driven by behaviors and actions.

2019 International Conference on Document Analysis and Recognition (ICDAR)

Neural Computing and Applications

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)

Graphomotor impressions are a product of complex cognitive, perceptual and motor skills and are w... more Graphomotor impressions are a product of complex cognitive, perceptual and motor skills and are widely used as psychometric tools for the diagnosis of a variety of neuro-psychological disorders. Apparent deformations in these responses are quantified as errors and are used are indicators of various conditions. Contrary to conventional assessment methods where manual analysis of impressions is carried out by trained clinicians, an automated scoring system is marked by several challenges. Prior to analysis, such computerized systems need to extract and recognize individual shapes drawn by subjects on a sheet of paper as an important pre-processing step. The aim of this study is to apply deep learning methods to recognize visual structures of interest produced by subjects. Experiments on figures of Bender Gestalt Test (BGT), a screening test for visuo-spatial and visuo-constructive disorders, produced by 120 subjects, demonstrate that deep feature representation brings significant improvements over classical approaches. The study is intended to be extended to discriminate coherent visual structures between produced figures and expected prototypes.

Neural Computing and Applications

To date, Artificial Intelligence systems for handwriting and drawing analysis have primarily targ... more To date, Artificial Intelligence systems for handwriting and drawing analysis have primarily targeted domains such as writer identification and sketch recognition. Conversely, the automatic characterization of graphomotor patterns as biomarkers of brain health is a relatively less explored research area. Despite its importance, the work done in this direction is limited and sporadic. This paper aims to provide a survey of related work to provide guidance to novice researchers and highlight relevant study contributions. The literature has been grouped into “visual analysis techniques” and “procedural analysis techniques”. Visual analysis techniques evaluate offline samples of a graphomotor response after completion. On the other hand, procedural analysis techniques focus on the dynamic processes involved in producing a graphomotor reaction. Since the primary goal of both families of strategies is to represent domain knowledge effectively, the paper also outlines the commonly employed...

Tenth International Conference on Graphics and Image Processing (ICGIP 2018)

Segmentation of constituent shapes is a vital yet challenging step in any automated sketch analys... more Segmentation of constituent shapes is a vital yet challenging step in any automated sketch analysis and interpretation system. Conventional stroke level sketch segmentation techniques perform well on a single shape, nevertheless, their performance degrades in a cluttered multi-object sample. On the contrary, object level techniques rely on proximity based perceptual grouping for shapes with disjoint constituent parts, which requires high level semantic knowledge. To overcome these challenges, we propose the use of state-of-the-art convolutional object detectors for the detection and segmentation of hand drawn shapes from offline samples of a neuropsychological drawing test i.e. Bender Gestalt Test (BGT). Experiments with different combinations of convolutional meta-architectures and feature extractors show that such networks can successfully be employed for sketch segmentation purposes even with limited training data and resources. Amongst all network combinations under evaluation in this study, Faster R-CNNs with ResNet-101 as feature extractor, outperform others by achieving precision, recall and F-measure values of 92.93%, 95.24% and 94.07% respectively.

2017 13th International Conference on Emerging Technologies (ICET), 2017

Smart phones with high resolution cameras have enabled innovative computer vision applications th... more Smart phones with high resolution cameras have enabled innovative computer vision applications that apply techniques like real-time image processing and virtual reality to provide a close approximation of reality. In this paper, we present an Android application that allows users to play a virtual piano by using a keyboard drawn on a piece of paper. The application allows the user to point the camera of a hand held device towards the keyboard, and process image of the paper keyboard in real time. The application then detects fingers placed on a key and after key detection plays the corresponding sounds. Initial results demonstrate the potential of such an application in providing a viable replacement for heavy and expensive instruments.

2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2020

Parkinson's disease (PD) is commonly characterized by several motor impairments like tremor, ... more Parkinson's disease (PD) is commonly characterized by several motor impairments like tremor, muscular rigidity and bradykinesia, that are collectively termed as ‘Parkinson's disease dysgraphia’. In an attempt to identify these motor-based Parkinsonian symptoms, experts have persistently been evaluating various dynamic attributes of handwriting, like pen pressure/position, stroke speed/trajectory, and on-surface/in-air time taken, captured with the help of online acquisition tools. Such devices not only capture various aspects of handwriting but provide rich sequential information that can be utilized to identify unique patterns from handwriting samples of PD patients. In this paper, we propose a model based on Bidirectional Gated Recurrent Units (BiGRU) to assess the potential of handwriting-based sequential information in the identification of Parkinsonian symptoms. One-dimensional convolution is applied to raw sequences and the resulting feature sequences are employed to train the BiGRU model for prediction. The results of our experiments validate the potential of our proposed technique in comparison to the state-of-the-art.

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), 2019

Source printer identification for printed document forgery detection has gained much significance... more Source printer identification for printed document forgery detection has gained much significance in recent years. Traditional techniques employed in the literature are highly text dependent and therefore may prove insufficient in certain scenarios. In this study, we present a text-independent approach for effective characterization of source printer, using deep visual features. By employing transfer learning on pre-trained Convolutional Neural Networks (CNNs), we achieve significant recognition results on a dataset of 1200 documents from 20 different printers (13 laser and 7 inkjet). A comparison with various conventional features on the same dataset demonstrate that our proposed methodology classifies printed documents more accurately and effectively.

2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2018

Expert Systems with Applications, 2021

Parkinson's disease (PD) is commonly characterized by several motor symptoms, such as bradykinesi... more Parkinson's disease (PD) is commonly characterized by several motor symptoms, such as bradykinesia, akinesia, rigidity, and tremor. The analysis of patients' fine motor control, particularly handwriting, is a powerful tool to support PD assessment. Over the years, various dynamic attributes of handwriting, such as pen pressure, stroke speed, in-air time, etc., which can be captured with the help of online handwriting acquisition tools, have been evaluated for the identification of PD. Motion events, and their associated spatio-temporal properties captured in online handwriting, enable effective classification of PD patients through the identification of unique sequential patterns. This paper proposes a novel classification model based on onedimensional convolutions and Bidirectional Gated Recurrent Units (BiGRUs) to assess the potential of sequential information of handwriting in identifying Parkinsonian symptoms. One-dimensional convolutions are applied to raw sequences as well as derived features; the resulting sequences are then fed to BiGRU layers to achieve the final classification. The proposed method outperformed state-of-the-art approaches on the PaHaW dataset and achieved competitive results on the NewHandPD dataset.

Document Analysis and Recognition – ICDAR 2021 Workshops, 2021

2018 International Conference on Frontiers of Information Technology (FIT), 2018

Historical manuscript dating is a challenging task that requires extensive domain knowledge regar... more Historical manuscript dating is a challenging task that requires extensive domain knowledge regarding varying scripts, writing styles and instruments involved. Increased digitization of historical documents for preservation and analysis purposes have encouraged the relevant community to design techniques for computerized dating of historical manuscripts. This paper presents a performance based comparative analysis of popular textural features for document dating purposes. Outcomes of a comprehensive series of experiments demonstrate that a combined textural features-based approach is more effective than employing individual texture features. A 345-dimensional feature vector is extracted by using a combination of individual features from Gabor filters, Uniform Local Binary Patterns and Histogram of Local Binary Patterns, whichisthenfedtoaLinear Discriminant Analysis (LDA) classifier. Performance evaluation of our proposed scheme on the Medieval Paleographical Scale (MPS) dataset gives a reduced Mean Absolute Error of 20.13.

2019 International Conference on Document Analysis and Recognition (ICDAR), 2019

Digitization of historical manuscripts from premodern eras, has captivated the document analysis ... more Digitization of historical manuscripts from premodern eras, has captivated the document analysis and pattern recognition community in recent years. Estimation of the period of production of such documents is a challenging yet favored research problem. In this paper, we present a deep learning based approach to effectively characterize the year of production of sample documents from the Medieval Paleographical Scale (MPS) dataset. By employing transfer learning on a number of popular pre-trained Convolutional Neural Network (CNN) models, we have significantly reduced the Mean Absolute Error (MAE) reported in previous studies.

2017 International Conference on Frontiers of Information Technology (FIT), 2017

Drawing tests have been long used by practitioners for early screening of a number of psychologic... more Drawing tests have been long used by practitioners for early screening of a number of psychological and neurological impairments. These brain functioning tests are used by psychologists to understand feelings, personality and reactions of individuals to different circumstances. Among these, Human Figure Drawing Test (HFDT) is a popular instrument for the assessment of cognitive functioning of individuals. While the HFDT has various dimensions, the focus of this study lies on the face of the drawn figure. A computerized system that analyzes the hand-drawn facial images to extract the expressions from the image is proposed. Sketch of human face is drawn by the subject and then fed to the system, the image is then binarized and segmented into different facial components. Features (based on local binary patterns, gray level co-occurrence matrices and histogram of oriented gradients) computed from the facial components are used to train an SVM classifier to learn to distinguish between four expression classes, ‘happy’, ‘sad’, ‘angry’ and ‘neutral’. The system evaluated on a custom developed database of sketches realized promising results. The developed system could serve as a useful module toward development of a complete automated system to score human figure drawing test.

Pattern Recognition Letters, 2018

Parkinson's disease (PD) is a degenerative disorder that progressively affects the central nervou... more Parkinson's disease (PD) is a degenerative disorder that progressively affects the central nervous system causing muscle rigidity, tremors, slowed movements and impaired balance. Sophisticated diagnostic procedures like SPECT scans can detect changes in the brain caused by PD but are only effective once the disease has advanced considerably. Analysis of subtle variations in handwriting and speech can serve as potential tools for early prediction of the disease. While traditional techniques mostly rely on dynamic (kinematic and spatio-temporal) features of handwriting, in this study, we quantitatively evaluate the visual attributes in characterization of graphomotor samples of PD patients. For this purpose, Convolutional Neural Networks are employed to extract discriminating visual features from multiple representations of various graphomotor samples produced by both control and PD subjects. The extracted features are then fed to a Support Vector Machine (SVM) classifier. Evaluations are carried out on a dataset of 72 subjects using early and late fusion techniques and an overall accuracy of 83% is realized with solely visual information.

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2016

Prediction of gender and other demographic attributes of individuals from handwriting samples off... more Prediction of gender and other demographic attributes of individuals from handwriting samples offers an interesting basic, as well as applied research problem. The correlation between gender and the visual appearance of handwriting has been validated by a number of studies and the present study is based on the same idea. We exploit the textural measurements as the discriminating attribute between male and female writings. The textural information in a writing is captured by applying a bank of Gabor filters to the image of handwriting. The mean and standard deviation values of the filter responses are collected in matrix and the Fourier transform of the matrix is used as a feature. Classification is carried out using a feed forward neural network. The proposed technique evaluated on a subset of the QUWI database realized promising results under different experimental settings.

2015 13th International Conference on Document Analysis and Recognition (ICDAR), 2015

Drawing tests have been long used by practitioners and researchers for early detection of psychol... more Drawing tests have been long used by practitioners and researchers for early detection of psychological and neurological impairments. These tests allow subjects to naturally express themselves as opposed to an interview or a written assessment. Bender Gestalt Test (BGT) is a well-known and established neurological test designed to detect signs of perceptual distortions. Subjects are shown a number of geometric patterns for reconstruction and assessments are made by observing properties like rotation, angulations, simplification and closure difficulty. The manual scoring of the test, however, is a time consuming and lengthy procedure especially when a large number of subjects is to be analyzed. This paper proposes the application of image analysis techniques to automatically score a subset of hand drawn images in the BGT test. A comparison of the scores reported by the automated system with those assigned by the psychologists not only reveals the effectiveness of the proposed system but also reflects the huge research potential this area possesses.

Document Analysis and Recognition – ICDAR 2021, 2021

Human-Robot Interaction - Theory and Application, Jul 4, 2018

Long-term companionship, emotional attachment and realistic interaction with robots have always b... more Long-term companionship, emotional attachment and realistic interaction with robots have always been the ultimate sign of technological advancement projected by sci-fi literature and entertainment industry. With the advent of artificial intelligence, we have indeed stepped into an era of socially believable robots or humanoids. Affective computing has enabled the deployment of emotional or social robots to a certain level in social settings like informatics, customer services and health care. Nevertheless, social believability of a robot is communicated through its physical embodiment and natural expressiveness. With each passing year, innovations in chemical and mechanical engineering have facilitated lifelike embodiments of robotics; however, still much work is required for developing a "social intelligence" in a robot in order to maintain the illusion of dealing with a real human being. This chapter is a collection of research studies on the modeling of complex autonomous systems. It will further shed light on how different social settings require different levels of social intelligence and what are the implications of integrating a socially and emotionally believable machine in a society driven by behaviors and actions.

2019 International Conference on Document Analysis and Recognition (ICDAR)

Neural Computing and Applications