Closed-Eye Gaze Gestures: Detection and Recognition of Closed-Eye Movements with Cameras in Smart Glasses

Findling, Rainhard Dieter; Nguyen, Le Ngu; Sigg, Stephan

doi:10.1007/978-3-030-20521-8_27

Cited by 6 publications

(11 citation statements)

References 14 publications

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“…In this section, we report the quantitative recognition performance results of RF, Bi-LSTM, and boosted-HMM algorithms. These algorithms are trained using the dataset from experiment in Section III as well as the HideMyGaze dataset [33]. From the experimental result, we obtained the following results: Table 5 depicted the evaluated performance results of RF for each complex gaze gesture.…”

Section: Resultsmentioning

confidence: 99%

“…Nevertheless, the boosted-HMM averagely outperformed the results of both RF and Bi-LSTM by 1.67% and 14.12% respectively. Similarly, we further use HideMyGaze datasets in [33] to evaluate our method on simple gaze gesture. Table 8 reported the evaluated performance results of RF for simple gaze gesture.…”

Section: Resultsmentioning

confidence: 99%

“…, 𝑔 " , 𝑜 # and 𝑜 " evaluated on selected window size w of the selected frames on each feature, for 𝑖 < 𝑤 ⩽ 𝑛. 2) OPTICAL FLOW FEATURE DECOMPOSITION We further utilized the closed-eyes HideMyGaze camera dataset in [33] to evaluate the performance of the proposed method. The dataset contains clustered gaze points obtained from optical flow approximations of simple gaze gestures.…”

Section: ) Denoisingmentioning

confidence: 99%

“…The sequence of 𝑂(𝑜 # , 𝑜 " ) features used in this work is the estimated head orientation from the eye tracker which is measured in radians and is found to correspond with computed head orientation angles in equation ( 3). In addition, gaze orientation angles from the dataset in [33] were computed as given in [30]:…”

Section: ) Denoisingmentioning

confidence: 99%

“…Thus, the dataset obtained from section-A was processed to learn the sequence of complex gaze gesture. The corresponding dataset in [33] was utilized to evaluate the performance of the method for similar models of simple gaze gestures set using ML algorithms.…”

Section: ) Denoisingmentioning

confidence: 99%

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Boosted Gaze Gesture Recognition Using Underlying Head Orientation Sequence

et al. 2023

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People find it challenging to control smart systems with complex gaze gestures due to the vulnerability of eye saccades. Instead, the existing works achieved good recognition accuracy of simple gaze gestures because of sufficient eye gaze points but simple gaze gestures have limited applications compared to complex gaze gestures. Complex gaze gestures need a composition of multiple subunits of eye fixation to contain a sequence of gaze points that are clustered and rotated with an underlying complex head orientation relationship. This paper proposes a new set of eye gaze points and head orientation angles as new sequences to recognize complex gaze gestures. Eye gaze points and head orientation angles have a powerful influence on gaze gesture formation. The new sequence was obtained by aligning clustered gaze points and head orientation angles with a simple moving average (SMA) to denoise and interpolate the gap between successive eye fixations. The aligned new sequence of complex gaze gestures was utilized to train sequential machine learning (ML) algorithms. To evaluate the performance of the proposed method, we recruited and recorded the eye gaze and head orientation features of ten participants using an eye tracker. The results show that Boosted Hidden Markov Models (HMM) using Random Subspace methods achieved the best accuracies of 94.72% and 98.1% for complex, and simple gestures respectively, which outperformed the conventional methods.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: ) Denoisingmentioning

confidence: 99%

Section: ) Denoisingmentioning

confidence: 99%

Section: ) Denoisingmentioning

confidence: 99%

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Boosted Gaze Gesture Recognition Using Underlying Head Orientation Sequence

et al. 2023

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

Bature,

Abdullahi,

Chiracharit

et al. 2024

IEEE Access

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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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Eyeball tracking in closed eyes from shadows

Sinharoy,

Dave,

et al. 2024

Real-Time Image Processing and Deep Learning 2024

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The capacity to track eyeball movements beneath closed eyelids holds significant promise across commercial, security, and medical domains. Our work presents a simple, effective, non-invasive method for closed-eye eyeball motion detection using videos. This method relies on detecting the temporal variations in eyelid shadows cast by the eyeball bulge in the subject's video following face alignment and video registration. We key points used for face alignment and video registration are the detected facial landmarks. The eye movement signals derived using the presented technique closely correlate with simultaneously captured electrooculography (EOG) signals. We showcase the potential of fusing the eyeball movement signals obtained thus with data acquired from ultra-wideband (UWB) or millimeter-wave (mmWave) Doppler sensors. This fusion, supported by machine learning-based algorithms, enables the classification of sleep stages in a smart sleep chair that is designed to enhance and extend good quality sleep.

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Closed-Eye Gaze Gestures: Detection and Recognition of Closed-Eye Movements with Cameras in Smart Glasses

Cited by 6 publications

References 14 publications

Boosted Gaze Gesture Recognition Using Underlying Head Orientation Sequence

Boosted Gaze Gesture Recognition Using Underlying Head Orientation Sequence

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

Eyeball tracking in closed eyes from shadows

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