Eye tracking is a process of measuring eye movement, determining where the person’s gaze is directed.
The result of an eye tracking study is an Attention Heatmap that shows how a group of participants viewed an image or video. Such eye movement data was needed to train our deep learning algorithms.
To carry out the eye tracking studies, we hired a neuromarketing lab which is a member of the International Neuromarketing Science and Business Association (NMSBA).
The Attention Insight algorithm is trained with approximately 70,000 individual participants’ data sets from eye tracking studies.
The image datasets we use are both open-source and proprietary.
Data set statistics:
Our heatmaps are generated by a deep learning algorithm called Convolutional Neural Network (CNN). It is a computing system that has an architecture inspired by the biological brain and mimics how neuron layers work.
A CNN consists of multiple layers of nodes that are connected with different weight connections. These weights determine how much one node impacts the following node.
Before the training, a CNN has random weight connections resulting in inaccurate heatmaps. The difference between the generated heatmap and the “ground truth” (actual eye tracking heatmaps) is called the error. During the many training cycles, these weights between layers are adjusted so that the error is reduced. After the training, our generated heatmaps match the “ground truth” closely.
We regularly add new data sets to make sure that our algorithm meets industry standards and stays the most accurate algorithm in the market.
To measure the accuracy of our generated heatmaps, we submitted our results to the Massachusetts Institute of Technology (MIT) saliency benchmark.
They sent us 300 test images, and we sent them back our model results on their testing data set of those 300 images.
After evaluating our results, MIT scientists concluded that our heatmaps match actual eye tracking heatmaps with 92.5% accuracy for general images. Across all types of designs, our heatmap accuracy is 90-96%
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