Saccade gaze prediction using a recurrent neural network
- Resource Type
- Conference
- Authors
- Ngo, Thuyen; Manjunath, B.S.
- Source
- 2017 IEEE International Conference on Image Processing (ICIP) Image Processing (ICIP), 2017 IEEE International Conference on. :3435-3439 Sep, 2017
- Subject
- Computing and Processing
Signal Processing and Analysis
Computational modeling
Feature extraction
Hidden Markov models
Logic gates
Training
Gaze tracking
Visualization
Eye tracking
scanpath
gaze
fixations
- Language
- ISSN
- 2381-8549
We present a model that generates close-to-human gaze sequences for a given image in the free viewing task. The proposed approach leverages recent advances in image recognition using convolutional neural networks and sequence modeling with recurrent neural networks. Feature maps from convolutional neural networks are used as inputs to a recurrent neural network. The recurrent neural network acts like a visual working memory that integrates the scene information and outputs a sequence of saccades. The model is trained end-to-end with real-world human eye-tracking data using back propagation and adaptive stochastic gradient descent. Overall, the proposed model is simple compared to the state-of-the-art methods while offering favorable performance on a standard eye-tracking data set.