Automatic Emotion Recognition through Handwriting Analysis: A Review

Kedar, Seema; Bormane, D. S.; Dhadwal, Aaditi; Alone, Shiwali; Agarwal, Rakesh

doi:10.1109/iccubea.2015.162

Cited by 23 publications

(7 citation statements)

References 9 publications

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“…On the other hand, the keyboard layout is designed for inputting English letters, for people whose native language is not English, they may find it is not easy to use, instead, writing on a touchscreen may be much easier than typing on a normal keyboard. Therefore, in the future, writing on a touchscreen may give users similar experience as writing in paper with a pen or brush [115,116]. For example, combining the traditional calligraphy with modern digital art provides a new way to create, present and preserve calligraphy works.…”

Section: E Recognizing Emotions Through Digital Writingmentioning

confidence: 99%

A Review of Emotion Recognition Methods From Keystroke, Mouse, and Touchscreen Dynamics

Yang

Qin²

2021

IEEE Access

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Emotion can be defined as a subject's organismic response to an external or internal stimulus event. The responses could be reflected in pattern changes of the subject's facial expression, gesture, gait, eye-movement, physiological signals, speech and voice, keystroke, and mouse dynamics, etc. This suggests that on the one hand emotions can be measured/recognized from the responses, and on the other hand they can be facilitated/regulated by external stimulus events, situation changes or internal motivation changes. It is well-known that emotion has a close relationship with both physical and mental health, usually affecting an individual's and a team's work performance, thus emotion recognition is an important prerequisite for emotion regulation towards better emotional states and work performance. The primary problem in emotion recognition is how to recognize a subject's emotional states easily and accurately. Currently, there are a body of good research on emotion recognition from facial expression, gesture, gait, eye-tracking, and other physiological signals such as speech and voice, but they are all intrusive and obtrusive to some extent. In contrast, keystroke, mouse and touchscreen (KMT) dynamics data can be collected non-intrusively and unobtrusively as secondary data responding to primary physical actions, thus, this paper aims to review the state-of-the-art research on emotion recognition from KMT dynamics and to identify key research challenges, opportunities and a future research roadmap for referencing. In addition, this paper answers the following six research questions (RQs): (1) what are the commonly used emotion elicitation methods and databases for emotion recognition? (2) which emotions could be recognized from KMT dynamics? (3) what key features are most appropriate for recognizing different specific emotions? (4) which classification methods are most effective for specific emotions? ( 5) what are the application trends of emotion recognition from KMT dynamics? (6) which application contexts are of greatest concern?

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Section: E Recognizing Emotions Through Digital Writingmentioning

confidence: 99%

A Review of Emotion Recognition Methods From Keystroke, Mouse, and Touchscreen Dynamics

Yang

Qin²

2021

IEEE Access

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“…Although every child in primary school learns how to write in a specific pattern and it becomes a motor skill as they mature but they can control what to write but not how to write. Therefore, analysis of one's handwriting and signature can reveal some information about the writer, such as behavioral traits, character or personality [38]. The handwriting and signature of individual can be measured and analyzed, therefore its measurability gives rise to opportunities for researchers to identify the identity of an individual as well as to predict their soft-biometric treats such as emotions [35], gender [39], age group [40], and assess the effects of alcohol [41].…”

Section: Introductionmentioning

confidence: 99%

“…Another study [38], is performed to identify people who are emotionally disturbed or depressed, and need psychological help. For this empirical study, offline handwriting features such as baseline, slant, pen-pressure, size, margin and zone is extracted from the scanned handwriting data.…”

Section: Introductionmentioning

confidence: 99%

Emotional State Prediction From Online Handwriting and Signature Biometrics

2019

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Handwriting and signature biometrics have a long history in the literature, especially in terms of identity recognition and/or verification; nevertheless, it reveals more information therefore provides more opportunities for personal characteristics estimation, particularly, emotional state. However, almost all publicly or commercially available databases do not include contributors' demographic labels or emotional status in addition to their identification labels, leading these available databases to be only useful for verification and identification based research studies. For this reason, this paper proposes both offline and online handwriting and signature biometric database with a wide range of ground truths (emotional status labels-happy, sad and stress) in addition to the identity labels and tries to predict ones' emotional state, namely happy, sad and stress, from their online biometric handwriting and signatures. The proposed database comprised total of 134 participants with 804 handwriting and 8040 signature biometric samples. The database presented also includes individuals' demographic information such as age, gender, handedness, education level and nationality. Subsequently, there are several experiments have been conducted, with different thresholds, which present the usability of the proposed database and preliminary results of emotional state prediction from both signature and handwriting biometrics. The experiments achieved remarkable success especially on stress prediction for handwriting. On the other hand, considering the results from signature biometrics, it is observed that happy and then sad and finally stress class forms the most part of the prediction accuracy.

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“…According to graphology, the writer's emotional state, as a higher-level characteristic of personality, can be detected at the time of writing (Grewal et al, 2012;Fairhurst et al, 2015). Kedar et al (2015) point out that handwriting features like baseline, slant (angulation), pen-pressure, size, margin and zone contribute to revealing one's emotions.…”

Section: Introductionmentioning

confidence: 99%

A Rendering Model for Emotional In-air Handwriting

Chen

Bourguet

Venture

2018

Electronic Workshops in Computing

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According to graphology, people's emotional states can be detected from their handwriting. The purpose of our research is to propose a 3D handwriting rendering model which has emotional capabilities. We developed an in-air handwriting system and selected eight emotions (afraid, angry, disgusted, happy, sad, surprised, amorous and serious) to be conveyed using five rendering parameters (weight, smoothness, tip of stroke, ink density and ink dryness). We then conducted an online survey to determine the optimal parameters' values. A first result shows that there is a positive correlation between the Euclidean distance between two emotions on the valence-arousal emotion two-dimensional model and the distance between the calculated rendering parameters' values. Another online survey then revealed that four of the emotions (afraid, sad, serious and angry) are well conveyed. The results are less conclusive for the other four emotions, but further work is being conducted to improve the model when the handwriting is shown in conjunction with the other emotional channels of a social robot.

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Automatic Emotion Recognition through Handwriting Analysis: A Review

Cited by 23 publications

References 9 publications

A Review of Emotion Recognition Methods From Keystroke, Mouse, and Touchscreen Dynamics

A Review of Emotion Recognition Methods From Keystroke, Mouse, and Touchscreen Dynamics

Emotional State Prediction From Online Handwriting and Signature Biometrics

A Rendering Model for Emotional In-air Handwriting

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