Towards Personality Classification Through Arabic Handwriting Analysis

Mostafa, Mohamed; Al‐Qurishi, Muhammad; Mathkour, Hassan

doi:10.1007/978-3-030-30809-4_51

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…In the realm of emotion detection, various state-of-the-art approaches have been explored, as exemplified by studies such as Rahman & Halim (2023) , Nolazco-Flores et al (2021) , Gavrilescu & Vizireanu (2018) , Mostafa, Al-Qurishi & Mathkour (2019) and Chitlangia & Malathi (2019) in Table 5 . These endeavors utilized different methodologies and sample sizes to decipher emotional states.…”

Section: Resultsmentioning

confidence: 99%

“…These endeavors utilized different methodologies and sample sizes to decipher emotional states. Specifically, Gavrilescu & Vizireanu (2018) , Mostafa, Al-Qurishi & Mathkour (2019) , and Chitlangia & Malathi (2019) employed artificial neural networks (ANN), decision trees, and k-NN with 128, 83, and 50 subjects, respectively. Meanwhile, Rahman & Halim (2023) employed combined features with BiLSTM, and Nolazco-Flores et al (2021) leveraged combined features with a Support Vector Machine (SVM).…”

Section: Resultsmentioning

confidence: 99%

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Emotion detection from handwriting and drawing samples using an attention-based transformer model

Khan,

Xia,

Aurangzeb

et al. 2024

PeerJ Computer Science

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Emotion detection (ED) involves the identification and understanding of an individual’s emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person’s emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).

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