Chetan Ralekar

Bio: Chetan Ralekar is an academic researcher from Indian Institute of Technology Delhi. The author has contributed to research in topics: Eye tracking & Deep learning. The author has an hindex of 4, co-authored 10 publications receiving 25 citations. Previous affiliations of Chetan Ralekar include Jaypee Institute of Information Technology.

Unlocking the Mechanism of Devanagari Letter Identification Using Eye Tracking

Abstract: The present day computers can outperform the human in many complicated tasks very precisely and efficiently. However, in many scenarios like pattern recognition and more importantly, character recognition; a school going child can outperform the sophisticated machines available today. The modern machines present today find handwritten, calligraphic text difficult to recognize because such texts hardly contain rationalized straight lines or perfect loops or circles. Therefore, most of the optical character recognition systems fail to recognize the characters beyond certain levels of distortions and noise. On the other hand, the human brain has achieved a remarkable ability to recognize visual patterns or characters in various distortion conditions with high speed. The present work tries to understand how human perceive, process and recognize the Devanagari characters under various distortion levels. In order to achieve this objective, eye tracking experiment was performed on 20 graduate participants by presenting stimuli in decreasing level of distortions (from highly distorted to more normal one). The eye fixation patterns along with the time course of recognition gave us the moment-to-moment processing involved in letter identification. Upon understanding the level of distortion acceptable for correct letter recognition and the processes involved in the identification of the letters, the OCR can be made more robust and the gap between human reading and machine reading can be narrowed down.

Emotion classification from EEG signals

Abstract: Emotions are the state of feelings resulting from physical and psychological change which in turn influence our behaviour. Emotions play an important role in corporate activities like organizing, planning, motivating etc. The negative emotions may lead to chronic emotional disorders like depression, anxiety, stress. Therefore, there is a need to analyse and classify these emotional changes through brain signals. The proposed paper aims to classify the types of emotions by observing EEG signals. There is always a problem of correlated data with EEG signals. This correlation results in redundancy of the data and increases computational time. This paper proposes the use of common spatial pattern to address the problem of correlated data and high dimensionality. The classification is carried out using support vector machine and the performance of the classifier is being measured in terms of accuracy of classification and standard deviation.

Effect of Devanagari Font Type in Reading Comprehension: An Eye Tracking Study

Abstract: In this world of digitization, screen reading has grown immensely due to the availability of affordable display devices. Most of the people prefer to read on display devices as compared to the print media. To make the reading experience of the reader pleasant and comfortable, the font designers strive hard to choose suitable typographical properties of the text such as font type, font size etc. Some of the researchers suggest that the typography of the text affects the reading performance of the readers to some extent. However, the research focusing on the effect of typography on the reading behavior of the readers is limited and it is hardly touched upon for the Indian scripts. Therefore, the proposed paper aims to find out the effect of Devanagari font type on the reading performance, especially reading comprehension of the readers. In addition to this, a method to reduce the error in the gaze estimation of the eye tracker is also proposed. In order to understand the reading behavior, an eye tracking experiment is performed on 14 participants asking them to read 22 pages, in 3 different font types, presented on the screen of the eye tracker. The performance of the readers is analyzed in terms of total reading time, comprehension score, number of fixations, fixation duration and number of regressions. Our results show that there is a significant difference in the fixation duration, a number of fixations and the comprehension score, when the same document is read in different font type. Thus, there is a scope for improvement in the reading comprehension, by changing the physical properties of the document without changing its content. These findings might be useful to understand the readers’ preference for the font and to design a proper font type for online reading.

Memorability-based image compression

Abstract: This study is concerned with achieving the image compression using the concept of memorability. The authors have used memorability of an image, as a perceptual measure while image coding. In the proposed approach, a region-of-interest-based memorability preserving image compression algorithm which is accomplished via two sub-processes namely, memorability prediction and image compression is introduced. The memorability of images is predicted using convolutional neural network and restricted Boltzmann machine features. Based on these features, the memorability score of individual patches in an image is calculated and these scores are used to generate the memorability map. These memorability map values are used for optimised image compression. In order to validate the results, an eye tracking experiment with human participants is performed. The comparative analysis shows that the memorability-based compression outperforms the state-of-the-art compression techniques.

Intelligent Identification of Ornamental Devanagari Characters Inspired by Visual Fixations

Abstract: This paper is concerned with the development of techniques for the recognition of ornamental characters motivated by the perceptual processes involved in humans. To understand the perceptual process, we have performed the eye-tracking experiment to recognize the special set of characters, with artistic variations in character structure and form. The novelty of this paper is the use of human visual fixations to supervise the intermediate layers of the convolutional neural network. From the results obtained, we found that the network performs better when trained with the assistance of fixation information compared to the network trained without eye fixations.

Emotions Recognition Using EEG Signals: A Survey

Abstract: Emotions have an important role in daily life, not only in human interaction, but also in decision-making processes, and in the perception of the world around us. Due to the recent interest shown by the research community in establishing emotional interactions between humans and computers, the identification of the emotional state of the former became a need. This can be achieved through multiple measures, such as subjective self-reports, autonomic and neurophysiological measurements. In the last years, Electroencephalography (EEG) received considerable attention from researchers, since it can provide a simple, cheap, portable, and ease-to-use solution for identifying emotions. In this paper, we present a survey of the neurophysiological research performed from 2009 to 2016, providing a comprehensive overview of the existing works in emotion recognition using EEG signals. We focus our analysis in the main aspects involved in the recognition process (e.g., subjects, features extracted, classifiers), and compare the works per them. From this analysis, we propose a set of good practice recommendations that researchers must follow to achieve reproducible, replicable, well-validated and high-quality results. We intend this survey to be useful for the research community working on emotion recognition through EEG signals, and in particular for those entering this field of research, since it offers a structured starting point.

Sensing system of environmental perception technologies for driverless vehicle: A review of state of the art and challenges

Abstract: Environmental perception technology is the guarantee of the safety of driverless vehicles. At present, there are a lot of researches and reviews on environmental perception, aiming to realize unmanned driving while ensuring the safety of human life. However, the technology is facing new challenges in the new era. This review paper attempts to systematically summarize environment perception technology and discuss the new challenges currently faced. To this end, we first summarized the advantages, disadvantages and applicable occasions of several commonly used sensing methods to provide a clear selection guide. The new challenges faced by environmental perception technology are discussed from three aspects: technology, external environment and applications. Finally, the article also points out the future development trends and efforts of environmental perception technology.

Emotion recognition and brain mapping for sentiment analysis: A review

Abstract: The rapid growth of the Internet has caused the increase in the amount of textual information available, such as in blogs, discussion forums and review sites on the web, where the texts surely have the emotion content Emotion is one appearence of people behaviour and it is an important performance in human computer interaction (HCI) Human express the emotion in the form of facial expression, speech and writing text Recently, researchers in computational linguistic (CL) areas are interested in the attention of emotion for Sentiment Analysis (SA) SA naturally observes the emotion conveyed by a text, and at the same time, distinguishing positive and negative valence The wide areas of CL research, actually considerable for investigating the emotion dimension detection and searching the approaches and techniques in the term of emotion recognition (ER) There are two significant trends of research in the area, the emotion recognition based on state affective computing and the real time using brain signal machines The two areas have the same aim for getting the improvement result in sentiment analysis with the mapping of emotion recognition provided The exclusive work on emotion detection is comparatively rare and lacks empirical evaluation research This paper provides the overview of past and recent research on emotion detection as well as some approaches and techniques used and shows the linked between both SA and ER

Effect of Devanagari Font Type in Reading Comprehension: An Eye Tracking Study

Abstract: In this world of digitization, screen reading has grown immensely due to the availability of affordable display devices. Most of the people prefer to read on display devices as compared to the print media. To make the reading experience of the reader pleasant and comfortable, the font designers strive hard to choose suitable typographical properties of the text such as font type, font size etc. Some of the researchers suggest that the typography of the text affects the reading performance of the readers to some extent. However, the research focusing on the effect of typography on the reading behavior of the readers is limited and it is hardly touched upon for the Indian scripts. Therefore, the proposed paper aims to find out the effect of Devanagari font type on the reading performance, especially reading comprehension of the readers. In addition to this, a method to reduce the error in the gaze estimation of the eye tracker is also proposed. In order to understand the reading behavior, an eye tracking experiment is performed on 14 participants asking them to read 22 pages, in 3 different font types, presented on the screen of the eye tracker. The performance of the readers is analyzed in terms of total reading time, comprehension score, number of fixations, fixation duration and number of regressions. Our results show that there is a significant difference in the fixation duration, a number of fixations and the comprehension score, when the same document is read in different font type. Thus, there is a scope for improvement in the reading comprehension, by changing the physical properties of the document without changing its content. These findings might be useful to understand the readers’ preference for the font and to design a proper font type for online reading.