S L Happy

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

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

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

IEEE Geoscience and Remote Sensing Letters

The lack of proper class discrimination among the Hyperspectral (HS) data points poses a potentia... more The lack of proper class discrimination among the Hyperspectral (HS) data points poses a potential challenge in HS classification. To address this issue, this paper proposes an optimal geometry-aware transformation for enhancing the classification accuracy. The underlying idea of this method is to obtain a linear projection matrix by solving a nonlinear objective function based on the intrinsic geometrical structure of the data. The objective function is constructed to quantify the discrimination between the points from dissimilar classes on the projected data space. Then the obtained projection matrix is used to linearly map the data to more discriminative space. The effectiveness of the proposed transformation is illustrated with three benchmark real-world HS data sets. The experiments reveal that the classification and dimensionality reduction methods on the projected discriminative space outperform their counterpart in the original space.

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ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks

IEEE Transactions on Geoscience and Remote Sensing

Due to the various reasons such as atmospheric effects and differences in acquisition, it is ofte... more Due to the various reasons such as atmospheric effects and differences in acquisition, it is often the case that there exists a large difference between spectral bands of satellite images collected from different geographic locations. The large shift between spectral distributions of training and test data causes the current state of the art supervised learning approaches to output unsatisfactory maps. We present a novel semantic segmentation framework that is robust to such shift. The key component of the proposed framework is Color Mapping Generative Adversarial Networks (ColorMapGAN), which can generate fake training images that are semantically exactly the same as training images, but whose spectral distribution is similar to the distribution of the test images. We then use the fake images and the ground-truth for the training images to fine-tune the already trained classifier. Contrary to the existing Generative Adversarial Networks (GANs), the generator in ColorMapGAN does not have any convolutional or pooling layers. It learns to transform the colors of the training data to the colors of the test data by performing only one element-wise matrix multiplication and one matrix addition operations. Thanks to the architecturally simple but powerful design of ColorMapGAN, the proposed framework outperforms the existing approaches with a large margin in terms of both accuracy and computational complexity.

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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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Spatial-Spectral Regularized Local Scaling Cut for Dimensionality Reduction in Hyperspectral Image Classification

IEEE Geoscience and Remote Sensing Letters

Dimensionality reduction (DR) methods have attracted extensive attention to provide discriminativ... more Dimensionality reduction (DR) methods have attracted extensive attention to provide discriminative information and reduce the computational burden of hyperspectral image (HSI) classification. However, the DR methods face many challenges due to limited training samples with high dimensional spectra. To address this issue, a graph-based spatial and spectral regularized local scaling cut (SSRLSC) for DR of HSI data is proposed. The underlying idea of the proposed method is to utilize the information from both the spectral and spatial domains to achieve better classification accuracy than its spectral domain counterpart. In SSRLSC, a guided filter is initially used to smoothen and homogenize the pixels of the HSI data in order to preserve the pixel consistency. This is followed by generation of between-class and within-class dissimilarity matrices in both spectral and spatial domains by regularized local scaling cut (RLSC) and neighboring pixel local scaling cut (NPLSC) respectively. Finally, we obtain the projection matrix by optimizing the updated spatial-spectral between-class and total-class dissimilarity. The effectiveness of the proposed DR algorithm is illustrated with two popular real-world HSI datasets. Index Terms-Dimensionality reduction, hyperspectral imaging, neighboring pixel local scaling cut, regularized local scaling cut, spatial spectral method.

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

ABSTRACT

An On-board Video Database of Human Drivers

ABSTRACT

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.

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

ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks

IEEE Transactions on Geoscience and Remote Sensing, 2020

Due to the various reasons such as atmospheric effects and differences in acquisition, it is ofte... more Due to the various reasons such as atmospheric effects and differences in acquisition, it is often the case that there exists a large difference between spectral bands of satellite images collected from different geographic locations. The large shift between spectral distributions of training and test data causes the current state of the art supervised learning approaches to output unsatisfactory maps. We present a novel semantic segmentation framework that is robust to such shift. The key component of the proposed framework is Color Mapping Generative Adversarial Networks (ColorMapGAN), which can generate fake training images that are semantically exactly the same as training images, but whose spectral distribution is similar to the distribution of the test images. We then use the fake images and the ground-truth for the training images to fine-tune the already trained classifier. Contrary to the existing Generative Adversarial Networks (GANs), the generator in ColorMapGAN does not have any convolutional or pooling layers. It learns to transform the colors of the training data to the colors of the test data by performing only one element-wise matrix multiplication and one matrix addition operations. Thanks to the architecturally simple but powerful design of ColorMapGAN, the proposed framework outperforms the existing approaches with a large margin in terms of both accuracy and computational complexity.

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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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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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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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Automated Alertness and Emotion Detection for Empathic Feedback during e-Learning

by Priyadarshi Patnaik and S L Happy

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&#39; performance through interaction, feedback, and positive mood induction.

The Indian Spontaneous Expression Database for Emotion Recognition

by Priyadarshi Patnaik and S L Happy

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.

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

by Anjith George and S L Happy

Drowsiness level detection of an individual is very important in many safety critical application... more Drowsiness level detection of an individual is very important in many safety critical applications such as driving. There are several invasive and contact based methods such as use of blood biochemical, brain signals etc. which can estimate the level of drowsiness very accurately. However, these methods are very difficult to implement in practical scenarios, as they cause discomfort to the user. This paper presents a combined voice and vision based drowsiness detection system well suited to detect the drowsiness level of an automotive driver. The vision and voice based detection, being non-contact methods, has the advantage of their feasibility of implementation. The authenticity of these methods have been cross-validated using brain signals.

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Automated Alertness and Emotion Detection for Empathic Feedback during e-Learning

by Priyadarshi Patnaik and S L Happy

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&#39; performance through interaction, feedback, and positive mood induction.

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by S L Happy and Anjith George

A Real Time Facial Expression Classification System Using Local Binary Patterns

by Anjith George and S L Happy

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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