Learning to Predict where the Children with Asd Look

As is known to us, people with Autism Spectrum Disorder (ASD) have atypical visual attention towards stimuli. Learning the visual attention of people especially, children, with ASD contribute to related research in the field of medicine and psychology. In this paper, we first construct a saliency prediction for children with autism (SPCA) database, which is the first of its kind and consists of 500 images and the corresponding eye tracking data collected from 13 different children with ASD. We compare the performance of five state-of-the-art deep neural networks (DNN)-based saliency prediction approaches with their original networks and the fine-tuned networks on our database. We predict the atypical visual attention of children with ASD for the first time and get the best saliency prediction results for individuals with ASD so far.

[1]  Qi Zhao,et al.  SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[2]  G. Dawson,et al.  Early recognition of children with autism: A study of first birthday home videotapes , 1994, Journal of autism and developmental disorders.

[3]  G. Dawson,et al.  Understanding the Nature of Face Processing Impairment in Autism: Insights From Behavioral and Electrophysiological Studies , 2005, Developmental neuropsychology.

[4]  Rita Cucchiara,et al.  Predicting Human Eye Fixations via an LSTM-Based Saliency Attentive Model , 2016, IEEE Transactions on Image Processing.

[5]  Xinfeng Zhang,et al.  Visual attention analysis and prediction on human faces , 2017, Inf. Sci..

[6]  Weisi Lin,et al.  Visual Saliency Detection With Free Energy Theory , 2015, IEEE Signal Processing Letters.

[7]  Xiongkuo Min,et al.  Fixation prediction through multimodal analysis , 2015, 2015 Visual Communications and Image Processing (VCIP).

[8]  Frédo Durand,et al.  What Do Different Evaluation Metrics Tell Us About Saliency Models? , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Noel E. O'Connor,et al.  SalGAN: Visual Saliency Prediction with Generative Adversarial Networks , 2017, ArXiv.

[10]  Rita Cucchiara,et al.  A deep multi-level network for saliency prediction , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[11]  Qi Zhao,et al.  Learning Visual Attention to Identify People with Autism Spectrum Disorder , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[12]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[13]  L. Itti,et al.  High-throughput classification of clinical populations from natural viewing eye movements , 2012, Journal of Neurology.

[14]  Li Yi,et al.  Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework , 2016, Autism research : official journal of the International Society for Autism Research.

[15]  Frédo Durand,et al.  Learning to predict where humans look , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[16]  Daniel P. Kennedy,et al.  Atypical Visual Saliency in Autism Spectrum Disorder Quantified through Model-Based Eye Tracking , 2015, Neuron.