A dataset of eye movements for the children with autism spectrum disorder

Duan, Huiyu; Zhai, Guangtao; Min, Xiongkuo; Che, Zhaohui; Fang, Yi; Yang, Xiaokang; Gutiérrez, Jesús; Callet, Patrick Le

doi:10.1145/3304109.3325818

Cited by 70 publications

(49 citation statements)

References 19 publications

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“…A very popular fixation point dataset containing 2000 images from 20 different categories has been proposed by [95]. Some research groups focused their efforts on gathering eye-movement data from cohort of people affected by some cognitive disorders such as ASD (Authism Spectrum Disorder) [96], [97], [98].…”

Section: B Eye-tracking Datasetsmentioning

confidence: 99%

A Multi-Scale Colour and Keypoint Density-Based Approach for Visual Saliency Detection

et al. 2020

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In the first seconds of observation of an image, several visual attention processes are involved in the identification of the visual targets that pop-out from the scene to our eyes. Saliency is the quality that makes certain regions of an image stand out from the visual field and grab our attention. Saliency detection models, inspired by visual cortex mechanisms, employ both colour and luminance features. Furthermore, both locations of pixels and presence of objects influence the Visual Attention processes. In this paper, we propose a new saliency method based on the combination of the distribution of interest points in the image with multiscale analysis, a centre bias module and a machine learning approach. We use perceptually uniform colour spaces to study how colour impacts on the extraction of saliency. To investigate eye-movements and assess the performances of saliency methods over object-based images, we conduct experimental sessions on our dataset ETTO (Eye Tracking Through Objects). Experiments show our approach to be accurate in the detection of saliency concerning state-of-the-art methods and accessible eye-movement datasets. The performances over object-based images are excellent and consistent on generic pictures. Besides, our work reveals interesting findings on some relationships between saliency and perceptually uniform colour spaces.

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Section: B Eye-tracking Datasetsmentioning

confidence: 99%

A Multi-Scale Colour and Keypoint Density-Based Approach for Visual Saliency Detection

et al. 2020

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“…First, our model was trained on the MIT1003 dataset [17] with an initial learning rate of 1e-4. The training set of ASD eye-tracking dataset [21] contains 300 images, which are split to 240, 30 and 30 for training, validation and testing, respectively. Then we finetuned the model on the first 240 images with a learning rate of 1e-6 and evaluated its performance on the validation set.…”

Section: Methodsmentioning

confidence: 99%

“…Comparison with state-of-the-art models on the test set with 30 images in the dataset[21]. The best two scores are marked in bold and underlined.…”

mentioning

confidence: 99%

Saliency Prediction via Multi-Level Features and Deep Supervision for Children with Autism Spectrum Disorder

Wei

Huang

et al. 2019

2019 IEEE International Conference on Multimedia &Amp; Expo Workshops (ICMEW)

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This paper proposes a novel saliency prediction model for children with autism spectrum disorder (ASD). Based on the convolutional neural network, the multi-level features are extracted and integrated to three attention maps, which are used to generate the predicted saliency map. The deep supervision on the attention maps is exploited to build connections between ground truths and the deep layers in the neural network during training. Furthermore, by performing the single-side clipping operation on the ground truths, our model is encouraged to enhance the capacity of better predicting the most salient regions in images. Experimental results on an ASD eye-tracking dataset demonstrate that our model achieves the better saliency prediction performance for children with ASD.

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“…Privacy & Security in Eye Tracking There is a growing concern in keeping eye movement data private and secure in both real-time applications [49], and published datasets [50]. Publicly available datasets release de-identified gaze data from individuals viewing VR videos [19], the social interactions of children with ASD [22], and individual responses to emotional content such as nude imagery and faces [69]. Sensitive information, such as personality traits [36] and neurological diagnoses [47], could be linked to individuals that contributed to the aggregate data.…”

Section: Introductionmentioning

confidence: 99%

The Security-Utility Trade-off for Iris Authentication and Eye Animation for Social Virtual Avatars

John

Jörg

Koppal

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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The gaze behavior of virtual avatars is critical to social presence and perceived eye contact during social interactions in Virtual Reality. Virtual Reality headsets are being designed with integrated eye tracking to enable compelling virtual social interactions. This paper shows that the near infra-red cameras used in eye tracking capture eye images that contain iris patterns of the user. Because iris patterns are a gold standard biometric, the current technology places the user's biometric identity at risk. Our first contribution is an optical defocus based hardware solution to remove the iris biometric from the stream of eye tracking images. We characterize the performance of this solution with different internal parameters. Our second contribution is a psychophysical experiment with a same-different task that investigates the sensitivity of users to a virtual avatar's eye movements when this solution is applied. By deriving detection threshold values, our findings provide a range of defocus parameters where the change in eye movements would go unnoticed in a conversational setting. Our third contribution is a perceptual study to determine the impact of defocus parameters on the perceived eye contact, attentiveness, naturalness, and truthfulness of the avatar. Thus, if a user wishes to protect their iris biometric, our approach provides a solution that balances biometric protection while preventing their conversation partner from perceiving a difference in the user's virtual avatar. This work is the first to develop secure eye tracking configurations for VR/AR/XR applications and motivates future work in the area.

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A dataset of eye movements for the children with autism spectrum disorder

Cited by 70 publications

References 19 publications

A Multi-Scale Colour and Keypoint Density-Based Approach for Visual Saliency Detection

A Multi-Scale Colour and Keypoint Density-Based Approach for Visual Saliency Detection

Saliency Prediction via Multi-Level Features and Deep Supervision for Children with Autism Spectrum Disorder

The Security-Utility Trade-off for Iris Authentication and Eye Animation for Social Virtual Avatars

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