S L Happy | Institut National de Recherche en Informatique et Automatique (INRIA) (original) (raw)

Papers by S L Happy

Research paper thumbnail of SEMI2I: Semantically Consistent Image-to-Image Translation for Domain Adaptation of Remote Sensing Data

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

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Research paper thumbnail of A Supervised Geometry-Aware Mapping Approach for Classification of Hyperspectral Images

IEEE Geoscience and Remote Sensing Letters

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Research paper thumbnail of ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks

IEEE Transactions on Geoscience and Remote Sensing

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Research paper thumbnail of A Semisupervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images

IEEE Transactions on Geoscience and Remote Sensing

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Research paper thumbnail of Spatial-Spectral Regularized Local Scaling Cut for Dimensionality Reduction in Hyperspectral Image Classification

IEEE Geoscience and Remote Sensing Letters

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Research paper thumbnail of A Drowsiness Detection Scheme Based on Fusion of Voice and Vision Cues

ABSTRACT

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Research paper thumbnail of An On-board Video Database of Human Drivers

ABSTRACT

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Research paper thumbnail of An on-board vision based system for drowsiness detection in automotive drivers

International Journal of Advances in Engineering Sciences and Applied Mathematics, 2013

ABSTRACT This paper proposes a system for on-board monitoring the loss of attention of an automot... more ABSTRACT This paper proposes a system for on-board monitoring the loss of attention of an automotive driver, based on PERcentage of eye CLOSure (PERCLOS). This system has been developed considering the practical on-board constraints such as illumination variation, poor illumination conditions, free movement of driver’s face, limitations in algorithms etc. A novel framework for PERCLOS computation is reported in this paper. The system consists of an embedded processing unit, a camera, a near infra-red lighting system, power supply, a set of speakers and a voltage regulation unit. The image based algorithm is based on the PERCLOS as an indicator of the loss of attention of the driver. The authenticity of PERCLOS as an indicator of drowsiness has been validated using EEG signals.

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Research paper thumbnail of A video database of human faces under near Infra-Red illumination for human computer interaction applications

2012 4th International Conference on Intelligent Human Computer Interaction (IHCI), 2012

ABSTRACT Human Computer Interaction (HCI) is an evolving area of research for coherent communicat... more ABSTRACT Human Computer Interaction (HCI) is an evolving area of research for coherent communication between computers and human beings. Some of the important applications of HCI as reported in literature are face detection, face pose estimation, face tracking and eye gaze estimation. Development of algorithms for these applications is an active field of research. However, availability of standard database to validate such algorithms is insufficient. This paper discusses the creation of such a database created under Near Infra-Red (NIR) illumination. NIR illumination has gained its popularity for night mode applications since prolonged exposure to Infra-Red (IR) lighting may lead to many health issues. The database contains NIR videos of 60 subjects in different head orientations and with different facial expressions, facial occlusions and illumination variation. This new database can be a very valuable resource for development and evaluation of algorithms on face detection, eye detection, head tracking, eye gaze tracking etc. in NIR lighting. Available at : https://sites.google.com/site/nirdatabase/home

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Research paper thumbnail of ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks

IEEE Transactions on Geoscience and Remote Sensing, 2020

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Research paper thumbnail of An Effective Feature Selection Method Based on Pair-Wise Feature Proximity for High Dimensional Low Sample Size Data

Feature selection has been studied widely in the literature. However, the efficacy of the selecti... more Feature selection has been studied widely in the literature. However, the efficacy of the selection criteria for low sample size applications is neglected in most cases. Most of the existing feature selection criteria are based on the sample similarity. However, the distance measures become insignificant for high dimensional low sample size (HDLSS) data. Moreover, the variance of a feature with a few samples is pointless unless it represents the data distribution efficiently. Instead of looking at the samples in groups, we evaluate their efficiency based on pairwise fashion. In our investigation, we noticed that considering a pair of samples at a time and selecting the features that bring them closer or put them far away is a better choice for feature selection. Experimental results on benchmark data sets demonstrate the effectiveness of the proposed method with low sample size, which outperforms many other state-of-the-art feature selection methods.

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Research paper thumbnail of An Unsupervised Approach for Overlapping Cervical Cell Cytoplasm Segmentation

The poor contrast and the overlapping of cervical cell cytoplasm are the major issues in the accu... more The poor contrast and the overlapping of cervical cell cytoplasm are the major issues in the accurate segmentation of cervical cell cytoplasm. This paper presents an automated unsupervised cytoplasm segmentation approach which can effectively find the cytoplasm boundaries in overlapping cells. The proposed approach first segments the cell clumps from the cervical smear image and detects the nuclei in each cell clump. A modified Otsu method with prior class probability is proposed for accurate segmentation of nuclei from the cell clumps. Using distance regularized level set evolution, the contour around each nucleus is evolved until it reaches the cytoplasm boundaries. Promising results were obtained by experimenting on ISBI 2015 challenge dataset.

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Research paper thumbnail of Fuzzy Histogram of Optical Flow Orientations for Micro-expression Recognition

In high-stake situations, the micro-expressions reveal the hidden emotions of a person and it has... more In high-stake situations, the micro-expressions reveal the hidden emotions of a person and it has potential applications in many areas. The recognition of such short-lived subtle expressions is a challenging task. The literature proposes several spatio-temporal features to encode the subtle changes on the face during a micro-expression. The spatial changes are almost indistinguishable as the facial appearance does not change appreciably. However, these changes possess a temporal pattern. This paper explores the temporal features associated with facial micro-movements and proposes fuzzy histogram of optical flow orientation (FHOFO) features for recognition of micro-expressions. The FHOFO constructs suitable angular histograms from optical flow vector orientations using histogram fuzzification to encode the temporal pattern for classifying the micro-expressions. We have also discussed the effect of inclusion and exclusion of the motion magnitudes during FHOFO feature extraction. It has been demonstrated by repeated experiments on the publicly available databases, that the performance of FHOFO is consistent and close or at times even better than the state-of-art techniques.

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Research paper thumbnail of Automated Alertness and Emotion Detection for Empathic Feedback during e-Learning

2013 IEEE Fifth International Conference on Technology for Education (t4e 2013), 2013

ABSTRACT In the context of education technology, empathic interaction with the user and feedback ... more ABSTRACT In the context of education technology, empathic interaction with the user and feedback by the learning system using multiple inputs such as video, voice and text inputs is an important area of research. In this paper, a non-intrusive, standalone model for intelligent assessment of alertness and emotional state as well as generation of appropriate feedback has been proposed. Using the non-intrusive visual cues, the system classifies emotion and alertness state of the user, and provides appropriate feedback according to the detected cognitive state using facial expressions, ocular parameters, postures, and gestures. Assessment of alertness level using ocular parameters such as PERCLOS and saccadic parameters, emotional state from facial expression analysis, and detection of both relevant cognitive and emotional states from upper body gestures and postures has been proposed. Integration of such a system in e-learning environment is expected to enhance students' performance through interaction, feedback, and positive mood induction.

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Research paper thumbnail of The Indian Spontaneous Expression Database for Emotion Recognition

IEEE Transactions on Affective Computing, 2015

Automatic recognition of spontaneous facial expressions is a major challenge in the field of affe... more Automatic recognition of spontaneous facial expressions is a major challenge in the field of affective computing. Head rotation, face pose, illumination variation, occlusion etc. are the attributes that increase the complexity of recognition of spontaneous expressions in practical applications. Effective recognition of expressions depends significantly on the quality of the database used. Most well-known facial expression databases consist of posed expressions. However, currently there is a huge demand for spontaneous expression databases for the pragmatic implementation of the facial expression recognition algorithms. In this paper, we propose and establish a new facial expression database containing spontaneous expressions of both male and female participants of Indian origin. The database consists of 428 segmented video clips of the spontaneous facial expressions of 50 participants. In our experiment, emotions were induced among the participants by using emotional videos and simultaneously their self-ratings were collected for each experienced emotion. Facial expression clips were annotated carefully by four trained decoders, which were further validated by the nature of stimuli used and self-report of emotions. An extensive analysis was carried out on the database using several machine learning algorithms and the results are provided for future reference. Such a spontaneous database will help in the development and validation of algorithms for recognition of spontaneous expressions.

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Research paper thumbnail of The Indian Spontaneous Expression Database for Emotion Recognition

Automatic recognition of spontaneous facial expressions is a major challenge in the field of affe... more Automatic recognition of spontaneous facial expressions is a major challenge in the field of affective computing. Head rotation, face pose, illumination variation, occlusion etc. are the attributes that increase the complexity of recognition of spontaneous expressions in practical applications. Effective recognition of expressions depends significantly on the quality of the database used. Most well-known facial expression databases consist of posed expressions. However, currently there is a huge demand for spontaneous expression databases for the pragmatic implementation of the facial expression recognition algorithms. In this paper, we propose and establish a new facial expression database containing spontaneous expressions of both male and female participants of Indian origin. The database consists of 428 segmented video clips of the spontaneous facial expressions of 50 participants. In our experiment, emotions were induced among the participants by using emotional videos and simultaneously their self-ratings were collected for each experienced emotion. Facial expression clips were annotated carefully by four trained decoders, which were further validated by the nature of stimuli used and self-report of emotions. An extensive analysis was carried out on the database using several machine learning algorithms and the results are provided for future reference. Such a spontaneous database will help in the development and validation of algorithms for recognition of spontaneous expressions.

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Research paper thumbnail of A Drowsiness Detection Scheme Based on Fusion of Voice and Vision Cues

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Research paper thumbnail of Automated Alertness and Emotion Detection for Empathic Feedback during e-Learning

2013 IEEE Fifth International Conference on Technology for Education (t4e 2013), 2013

ABSTRACT In the context of education technology, empathic interaction with the user and feedback ... more ABSTRACT In the context of education technology, empathic interaction with the user and feedback by the learning system using multiple inputs such as video, voice and text inputs is an important area of research. In this paper, a non-intrusive, standalone model for intelligent assessment of alertness and emotional state as well as generation of appropriate feedback has been proposed. Using the non-intrusive visual cues, the system classifies emotion and alertness state of the user, and provides appropriate feedback according to the detected cognitive state using facial expressions, ocular parameters, postures, and gestures. Assessment of alertness level using ocular parameters such as PERCLOS and saccadic parameters, emotional state from facial expression analysis, and detection of both relevant cognitive and emotional states from upper body gestures and postures has been proposed. Integration of such a system in e-learning environment is expected to enhance students' performance through interaction, feedback, and positive mood induction.

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Research paper thumbnail of link

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Research paper thumbnail of A Real Time Facial Expression Classification System Using Local Binary Patterns

Facial expression analysis is one of the popular fields of research in human computer interaction... more Facial expression analysis is one of the popular fields of
research in human computer interaction (HCI). It has several
applications in next generation user interfaces, human emotion
analysis, behavior and cognitive modeling. In this paper, a facial
expression classification algorithm is proposed which uses Haar
classifier for face detection purpose, Local Binary Patterns (LBP)
histogram of different block sizes of a face image as feature vectors
and classifies various facial expressions using Principal
Component Analysis (PCA). The algorithm is implemented in real
time for expression classification since the computational
complexity of the algorithm is small. A customizable approach is
proposed for facial expression analysis, since the various
expressions and intensity of expressions vary from person to
person. The system uses grayscale frontal face images of a person
to classify six basic emotions namely happiness, sadness, disgust,
fear, surprise and anger

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Research paper thumbnail of SEMI2I: Semantically Consistent Image-to-Image Translation for Domain Adaptation of Remote Sensing Data

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

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Research paper thumbnail of A Supervised Geometry-Aware Mapping Approach for Classification of Hyperspectral Images

IEEE Geoscience and Remote Sensing Letters

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Research paper thumbnail of ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks

IEEE Transactions on Geoscience and Remote Sensing

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Research paper thumbnail of A Semisupervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images

IEEE Transactions on Geoscience and Remote Sensing

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Research paper thumbnail of Spatial-Spectral Regularized Local Scaling Cut for Dimensionality Reduction in Hyperspectral Image Classification

IEEE Geoscience and Remote Sensing Letters

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Research paper thumbnail of A Drowsiness Detection Scheme Based on Fusion of Voice and Vision Cues

ABSTRACT

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Research paper thumbnail of An On-board Video Database of Human Drivers

ABSTRACT

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An on-board vision based system for drowsiness detection in automotive drivers

International Journal of Advances in Engineering Sciences and Applied Mathematics, 2013

ABSTRACT This paper proposes a system for on-board monitoring the loss of attention of an automot... more ABSTRACT This paper proposes a system for on-board monitoring the loss of attention of an automotive driver, based on PERcentage of eye CLOSure (PERCLOS). This system has been developed considering the practical on-board constraints such as illumination variation, poor illumination conditions, free movement of driver’s face, limitations in algorithms etc. A novel framework for PERCLOS computation is reported in this paper. The system consists of an embedded processing unit, a camera, a near infra-red lighting system, power supply, a set of speakers and a voltage regulation unit. The image based algorithm is based on the PERCLOS as an indicator of the loss of attention of the driver. The authenticity of PERCLOS as an indicator of drowsiness has been validated using EEG signals.

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Research paper thumbnail of A video database of human faces under near Infra-Red illumination for human computer interaction applications

2012 4th International Conference on Intelligent Human Computer Interaction (IHCI), 2012

ABSTRACT Human Computer Interaction (HCI) is an evolving area of research for coherent communicat... more ABSTRACT Human Computer Interaction (HCI) is an evolving area of research for coherent communication between computers and human beings. Some of the important applications of HCI as reported in literature are face detection, face pose estimation, face tracking and eye gaze estimation. Development of algorithms for these applications is an active field of research. However, availability of standard database to validate such algorithms is insufficient. This paper discusses the creation of such a database created under Near Infra-Red (NIR) illumination. NIR illumination has gained its popularity for night mode applications since prolonged exposure to Infra-Red (IR) lighting may lead to many health issues. The database contains NIR videos of 60 subjects in different head orientations and with different facial expressions, facial occlusions and illumination variation. This new database can be a very valuable resource for development and evaluation of algorithms on face detection, eye detection, head tracking, eye gaze tracking etc. in NIR lighting. Available at : https://sites.google.com/site/nirdatabase/home

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Research paper thumbnail of ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks

IEEE Transactions on Geoscience and Remote Sensing, 2020

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Research paper thumbnail of An Effective Feature Selection Method Based on Pair-Wise Feature Proximity for High Dimensional Low Sample Size Data

Feature selection has been studied widely in the literature. However, the efficacy of the selecti... more Feature selection has been studied widely in the literature. However, the efficacy of the selection criteria for low sample size applications is neglected in most cases. Most of the existing feature selection criteria are based on the sample similarity. However, the distance measures become insignificant for high dimensional low sample size (HDLSS) data. Moreover, the variance of a feature with a few samples is pointless unless it represents the data distribution efficiently. Instead of looking at the samples in groups, we evaluate their efficiency based on pairwise fashion. In our investigation, we noticed that considering a pair of samples at a time and selecting the features that bring them closer or put them far away is a better choice for feature selection. Experimental results on benchmark data sets demonstrate the effectiveness of the proposed method with low sample size, which outperforms many other state-of-the-art feature selection methods.

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Research paper thumbnail of An Unsupervised Approach for Overlapping Cervical Cell Cytoplasm Segmentation

The poor contrast and the overlapping of cervical cell cytoplasm are the major issues in the accu... more The poor contrast and the overlapping of cervical cell cytoplasm are the major issues in the accurate segmentation of cervical cell cytoplasm. This paper presents an automated unsupervised cytoplasm segmentation approach which can effectively find the cytoplasm boundaries in overlapping cells. The proposed approach first segments the cell clumps from the cervical smear image and detects the nuclei in each cell clump. A modified Otsu method with prior class probability is proposed for accurate segmentation of nuclei from the cell clumps. Using distance regularized level set evolution, the contour around each nucleus is evolved until it reaches the cytoplasm boundaries. Promising results were obtained by experimenting on ISBI 2015 challenge dataset.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Fuzzy Histogram of Optical Flow Orientations for Micro-expression Recognition

In high-stake situations, the micro-expressions reveal the hidden emotions of a person and it has... more In high-stake situations, the micro-expressions reveal the hidden emotions of a person and it has potential applications in many areas. The recognition of such short-lived subtle expressions is a challenging task. The literature proposes several spatio-temporal features to encode the subtle changes on the face during a micro-expression. The spatial changes are almost indistinguishable as the facial appearance does not change appreciably. However, these changes possess a temporal pattern. This paper explores the temporal features associated with facial micro-movements and proposes fuzzy histogram of optical flow orientation (FHOFO) features for recognition of micro-expressions. The FHOFO constructs suitable angular histograms from optical flow vector orientations using histogram fuzzification to encode the temporal pattern for classifying the micro-expressions. We have also discussed the effect of inclusion and exclusion of the motion magnitudes during FHOFO feature extraction. It has been demonstrated by repeated experiments on the publicly available databases, that the performance of FHOFO is consistent and close or at times even better than the state-of-art techniques.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Automated Alertness and Emotion Detection for Empathic Feedback during e-Learning

2013 IEEE Fifth International Conference on Technology for Education (t4e 2013), 2013

ABSTRACT In the context of education technology, empathic interaction with the user and feedback ... more ABSTRACT In the context of education technology, empathic interaction with the user and feedback by the learning system using multiple inputs such as video, voice and text inputs is an important area of research. In this paper, a non-intrusive, standalone model for intelligent assessment of alertness and emotional state as well as generation of appropriate feedback has been proposed. Using the non-intrusive visual cues, the system classifies emotion and alertness state of the user, and provides appropriate feedback according to the detected cognitive state using facial expressions, ocular parameters, postures, and gestures. Assessment of alertness level using ocular parameters such as PERCLOS and saccadic parameters, emotional state from facial expression analysis, and detection of both relevant cognitive and emotional states from upper body gestures and postures has been proposed. Integration of such a system in e-learning environment is expected to enhance students' performance through interaction, feedback, and positive mood induction.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of The Indian Spontaneous Expression Database for Emotion Recognition

IEEE Transactions on Affective Computing, 2015

Automatic recognition of spontaneous facial expressions is a major challenge in the field of affe... more Automatic recognition of spontaneous facial expressions is a major challenge in the field of affective computing. Head rotation, face pose, illumination variation, occlusion etc. are the attributes that increase the complexity of recognition of spontaneous expressions in practical applications. Effective recognition of expressions depends significantly on the quality of the database used. Most well-known facial expression databases consist of posed expressions. However, currently there is a huge demand for spontaneous expression databases for the pragmatic implementation of the facial expression recognition algorithms. In this paper, we propose and establish a new facial expression database containing spontaneous expressions of both male and female participants of Indian origin. The database consists of 428 segmented video clips of the spontaneous facial expressions of 50 participants. In our experiment, emotions were induced among the participants by using emotional videos and simultaneously their self-ratings were collected for each experienced emotion. Facial expression clips were annotated carefully by four trained decoders, which were further validated by the nature of stimuli used and self-report of emotions. An extensive analysis was carried out on the database using several machine learning algorithms and the results are provided for future reference. Such a spontaneous database will help in the development and validation of algorithms for recognition of spontaneous expressions.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of The Indian Spontaneous Expression Database for Emotion Recognition

Automatic recognition of spontaneous facial expressions is a major challenge in the field of affe... more Automatic recognition of spontaneous facial expressions is a major challenge in the field of affective computing. Head rotation, face pose, illumination variation, occlusion etc. are the attributes that increase the complexity of recognition of spontaneous expressions in practical applications. Effective recognition of expressions depends significantly on the quality of the database used. Most well-known facial expression databases consist of posed expressions. However, currently there is a huge demand for spontaneous expression databases for the pragmatic implementation of the facial expression recognition algorithms. In this paper, we propose and establish a new facial expression database containing spontaneous expressions of both male and female participants of Indian origin. The database consists of 428 segmented video clips of the spontaneous facial expressions of 50 participants. In our experiment, emotions were induced among the participants by using emotional videos and simultaneously their self-ratings were collected for each experienced emotion. Facial expression clips were annotated carefully by four trained decoders, which were further validated by the nature of stimuli used and self-report of emotions. An extensive analysis was carried out on the database using several machine learning algorithms and the results are provided for future reference. Such a spontaneous database will help in the development and validation of algorithms for recognition of spontaneous expressions.

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Research paper thumbnail of A Drowsiness Detection Scheme Based on Fusion of Voice and Vision Cues

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Automated Alertness and Emotion Detection for Empathic Feedback during e-Learning

2013 IEEE Fifth International Conference on Technology for Education (t4e 2013), 2013

ABSTRACT In the context of education technology, empathic interaction with the user and feedback ... more ABSTRACT In the context of education technology, empathic interaction with the user and feedback by the learning system using multiple inputs such as video, voice and text inputs is an important area of research. In this paper, a non-intrusive, standalone model for intelligent assessment of alertness and emotional state as well as generation of appropriate feedback has been proposed. Using the non-intrusive visual cues, the system classifies emotion and alertness state of the user, and provides appropriate feedback according to the detected cognitive state using facial expressions, ocular parameters, postures, and gestures. Assessment of alertness level using ocular parameters such as PERCLOS and saccadic parameters, emotional state from facial expression analysis, and detection of both relevant cognitive and emotional states from upper body gestures and postures has been proposed. Integration of such a system in e-learning environment is expected to enhance students' performance through interaction, feedback, and positive mood induction.

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Research paper thumbnail of link

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Research paper thumbnail of A Real Time Facial Expression Classification System Using Local Binary Patterns

Facial expression analysis is one of the popular fields of research in human computer interaction... more Facial expression analysis is one of the popular fields of
research in human computer interaction (HCI). It has several
applications in next generation user interfaces, human emotion
analysis, behavior and cognitive modeling. In this paper, a facial
expression classification algorithm is proposed which uses Haar
classifier for face detection purpose, Local Binary Patterns (LBP)
histogram of different block sizes of a face image as feature vectors
and classifies various facial expressions using Principal
Component Analysis (PCA). The algorithm is implemented in real
time for expression classification since the computational
complexity of the algorithm is small. A customizable approach is
proposed for facial expression analysis, since the various
expressions and intensity of expressions vary from person to
person. The system uses grayscale frontal face images of a person
to classify six basic emotions namely happiness, sadness, disgust,
fear, surprise and anger

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Research paper thumbnail of Graph Scaling Cut with L1-Norm for Classification of Hyperspectral Images

In this paper, we propose an L1 normalized graph based dimensionality reduction method for Hypers... more In this paper, we propose an L1 normalized graph based dimensionality reduction method for Hyperspectral images, called as ‘L1-Scaling Cut’ (L1-SC). The underlying idea of this method is to generate the optimal projection matrix by retaining the original distribution of the data. Though L2-norm is generally preferred for computation, it is sensitive to noise and outliers. However, L1-norm is robust to them. Therefore, we obtain the optimal projection matrix by maximizing the ratio of between-class dispersion to within-class dispersion using L1- norm. Furthermore, an iterative algorithm is described to solve the optimization problem. The experimental results of the HSI classification confirm the effectiveness of the proposed L1-SC method on both noisy and noiseless data.

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Research paper thumbnail of A Real-time Robust Facial Expression Recognition System using HOG Features

This paper presents a facial expression recognition framework which infers the emotional states i... more This paper presents a facial expression recognition framework which infers the emotional states in real-time, thereby enabling the computers to interact more intelligently with people. The proposed method determines the face as well as the facial landmark points, extracts discriminating features from suitable facial regions, and classifies the expressions in real-time from live webcam feed. The speed of the system is improved by the appropriate combination of the detection and tracking algorithms. Further, instead of the whole face, histogram of oriented gradients (HOG) features are extracted from the active facial patches which makes the system robust against the scale and pose variations. The feature vectors are further fed to a support vector machine (SVM) classifier to classify into neutral or six universal expressions. Experimental results show an accuracy of 95% with 5 folds cross-validation in extended Cohn-Kanade (CK+) dataset

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