Boosted Gaze Gesture Recognition Using Underlying Head Orientation Sequence

Bature, Zakariyya Abdullahi; Abdullahi, Sunusi Bala; Yeamkuan, Suparat; Chiracharit, Werapon; Chamnongthai, Kosin

doi:10.1109/access.2023.3270285

Cited by 4 publications

(5 citation statements)

References 39 publications

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“…The proposed method achieved an average accuracy of 96.20%, precision of 98.16%, recall of 97.86%, and F1-score of 97.95% performance. Table IV shows the evaluation results of the proposed method on complex eye gestures [59]. The results show a good recognition performance of 100% for complex gestures such as "infinity", "Question mark", "Rectangle", "Triangle", and "Z" gestures.…”

Section: Resultsmentioning

confidence: 96%

“…The following results were obtained from the recognition of Japanese character datasets [42], HideMyGaze [58], and complex gaze gesture datasets [59]. Table III shows the evaluation results of the proposed method on HideMyGaze [58].…”

Section: Resultsmentioning

confidence: 99%

“…For the comparison of our experimental results, we adopted publicly available datasets to compare our proposed method with some existing methods in Table III-V. HideMyGaze dataset was used to evaluate the performance of the proposed method with RF and GCN methods in [60], BiLSTM, and Boosted HMMs methods in [59] as shown in Figure 7. Figure 8 depicts the comparison between the proposed method and other sequential ML methods such as SVM, BiLSTM, and Boosted HMMs using a complex gaze gesture dataset in [59]. Figure 9 depicts the evaluated performance of the proposed methods using Japanese Katakana dataset and other baseline methods such as DTW and HMM [42] and CNN [43].…”

Section: A Comparison With Some Baseline Methods Using Existing Datasetsmentioning

confidence: 99%

“…The datasets in [59] were recorded with Tobii eye tracker 5 and captured the eye gaze and head orientation angles of 10 users performing complex gestures. The complex gesture consisted of 9 selected gestures that are difficult for the eye to perform due to the curvy and zigzag in their patterns.…”

Section: ) Complex Gaze Gesture Datasetsmentioning

confidence: 99%

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Translated Pattern-Based Eye-Writing Recognition Using Dilated Causal Convolution Network

Bature,

Abdullahi,

Chiracharit

et al. 2024

IEEE Access

Self Cite

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Recently, eye-writing has been used as a novel language communication method, in which the paths of eye movement are detected for character recognition. However, instability of the eyes causes gaze points to form a character with a non-uniform shape and distinct writing style across participants. The non-uniformity affected the performance of the recognition algorithms and set back the applicability of eyewriting. In this paper root translation and dilated causal convolutional (DCC) layers are utilized to model the non-uniformity in eye-writing patterns. The root translation shifted the pattern to have uniform root gaze points by obtaining the difference between the initial gaze points and subsequent gaze points. The translated patterns were used to train the temporal convolution network (TCN) having three stacked DCC layers with different filter and dilation factors. The DCC layers extract temporal dependencies in the pattern by convolving a particular gaze point with a certain previous gaze point within its receptive field. To evaluate the performance of the proposed method, a dataset of 36 eye-writing characters including 26 English and 10 Arabic numerals was recorded from 20 participants using Tobii eye tracker. The evaluation results depicted that our proposed method achieved an accuracy of 96.20% on our newly designed English and Arabic numeral datasets. The proposed method outperform conventional methods by achieving 98.81%, 97.76%, and 93.51% on HideMyGaze, Complex gaze gesture, and Isolated Japanese Katakana datasets, respectively.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Section: A Comparison With Some Baseline Methods Using Existing Datasetsmentioning

confidence: 99%

Section: ) Complex Gaze Gesture Datasetsmentioning

confidence: 99%

See 2 more Smart Citations

Translated Pattern-Based Eye-Writing Recognition Using Dilated Causal Convolution Network

Bature,

Abdullahi,

Chiracharit

et al. 2024

IEEE Access

Self Cite

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“…The forest becomes adaptive and produces more reliable ndings if there are several randomly arranged trees in the forest [48]. Random forest is very effective in handling over tting, modeling complex features, and handling the presence of outliers [49].…”

Section: Random Forestmentioning

confidence: 99%

Modeling the Dynamic Behaviors of Bank Account Fraudsters Using Combined Simultaneous Game Theory with Neural Networks

USMAN,

Abdullahi,

Ran

et al. 2024

Preprint

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New bank account fraud is a significant problem causing financial losses in banking and finance. Existing statistical and machine-learning methods were used to detect fraud thereby preventing financial losses. However, most studies do not consider the dynamic behavior of fraudsters and often produce a high False Positive Rate (FPR). This study proposes the detection of new bank account fraud in the context of simultaneous game theory (SGT) with Neural Networks, the SGT involves two players, a fraudster, and bank officials attacking each other through Bayesian probability in a zero-sum. The influence of outliers within the SGT was tackled by adding a context feature for effective simulation of the dynamic behavior of fraudsters. The Neural Networks layer uses the simulated features for fraud context learning. The study is validated using Bank Account Fraud (BAF) Dataset on different machine-learning models. The Radial Basis Function Networks achieved FPR of 0.0% and 8.3% for fraud and non-fraud classes, respectively, while achieving True Positive Rate (TPR) of 91.7% and 100.0% for fraud and non-fraud classes, respectively. An improved Radial Basis Function Networks detect fraud by revealing fraudulent patterns and dynamic behaviors in higher dimensional data. The findings will enhance fraud detection and reduce customer attrition.

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