{ "fetched_at": "2026-10-24T17:12:12Z", "source_url": "https://www.kaggle.com/competitions/emvic/data", "pages": [ { "name": "Description", "content": "

The aim of the contest is to determine how people may be identified based on their eye movement characteristic. The organizers provide all interested participants dataset of eye movements' recordings in CSV format. After downloading the training dataset, participants may analyze it to prepare their own classification models and try to classify sapmles in test dataset.

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This in an official competition for BTAS 2012 (The Fifth IEEE International Conference on Biometrics: Theory, Applications and Systems, September 23-27, Washington DC, USA) and all results will be published during that conference (and of course on this web page as well).

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To become a participant you don't need to have any special eye tracking equipment. All data needed is ready to download! You only need to have some experience in data classification and... take your chance.

", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "rules", "content": "\r\n\t\r\n

One account per participant

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You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.

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No private sharing outside teams

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\r\n Privately sharing code or data outside of teams is not permitted.\r\n It's okay to share code if made available to all participants on the forums.\r\n

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Public dissemination of entries

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Kaggle and the competition host have the right to publicly disseminate any entries or models.

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Open licensing of winners

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\r\n Winning solutions need to be made available under\r\n a popular OSI-approved license\r\n in order to be eligible for recognition and prize money.\r\n

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Winning solutions must be posted or linked to in the forums.

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\r\n Prizes will be awarded after the winners have posted their solutions to the competition forum.\r\n Winners must post or link to their solutions within seven (7) days of being notified of their winning status.\r\n

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Team Mergers

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\r\n Team mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the merge date. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.
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Team Limits

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There is no maximum team size.

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Submission Limits

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You may submit a maximum of 2 entries per day.

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You may select up to 5 final submissions for judging.

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Competition Timeline

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Start Date: 3/20/2012 4:29 AM UTC

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Merger Deadline: None

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Entry Deadline: None

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End Date: 4/15/2012 11:59 PM UTC

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Prospective competitors may register on the web page and download dataset. The dataset consists of two parts: training data - set of known (labeled) samples, and test data - set of samples with unknown classification (unlabeled). Competitors may analyze labeled data, build classifiers and try to classify unlabeled samples from test data file. Tests results and description of methodologies will be published on this web page and will be the subject of presentation during BTAS 2012 (The Fifth IEEE International Conference on Biometrics: Theory, Applications and Systems, September 23-27, Washington DC, USA). The best results will be rewarded (the amount of the prize is still under negotiation with founders).

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This competition is a part of the whole Eye Movement Verification and Identification Competition which is available at www.emvic.org.

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Code for benchmarks

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Dataset is stored in simple CSV format where first column is classification (0 or 1) and all other columns are values obtained from eye tracker. 

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The dataset consists of 978 samples from 37 subjects. Every sample is labeled with 1 (it belongs to one chosen specific person) or 0 (it belongs to someone else). Samples were taken with 250Hz frequency using Ober2 eye tracker. As it was 2048 measures taken, the whole measurements lasted 8192 ms. There was a jumping point on 3x3 matrix used as stimulus. The stimulus consists of eleven point position changes giving twelve consecutive point positions. First point appears in the middle of the screen and the person should look at it with eyes positioned directly ahead. After 1600 ms the point in the middle disappears and for 20 ms a screen is blank. In that time eyes are in instable state waiting for another point of interest. Then the point appears in the upper right corner. The flashing point on the blank screen attracts eyes attention even without the person's will. The 'jumps' of the point continue until the last point position in the middle of the screen is reached. 
\"Dataset

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Datasets downloadable for competition are available in CSV format. It is a text file with one line for every sample. Every line is a list of comma separated elements as follows:

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class lx rx ly ry
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where:

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The values are 0 for point in the middle, positive for point on the right or upper side of the screen and negative for points on the left or lower side of the screen. http://www.kasprowski.pl/emvic/stimFile.txt

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Final remark

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The dataset was collected at Silesian University of Technology, Poland by Dr. Paweł Kasprowski.

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All data is published for purpose of competition only. However, if you intend to use the data in your future research you may do it only if the databases are acknowledged with the following reference:

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KASPROWSKI, P., OBER, J. 2004. Eye Movement in Biometrics, In Proceedings of Biometric Authentication Workshop, European Conference on Computer Vision in Prague 2004, LNCS 3087, Springer-Verlag.the IEEE/IARP International Conference on Biometrics (ICB), pp. 1-8.

", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "

Predicted probabilities that a subject generated the eye movement sample are evaluated using the log loss metric.

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Log loss is defined as:

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$$\\text{log loss}=-\\frac{1}{N}\\sum_{i=1}^N\\sum_{j=1}^My_{i,j}\\log\\left(\\hat{y_{i,j}}\\right),$$

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where \\\\(N\\\\) is the number of samples, \\\\(M\\\\) is the number of subjects, \\\\(\\text{log}\\\\) is the natural logarithm, \\\\(\\hat{y_{i,j}}\\\\) is the posterior probability that the \\\\(j^{th}\\\\) subject generated the \\\\(i^{th}\\\\) sample, and \\\\(y_{i,j}\\\\) is the ground truth (\\\\(y_{i,j}=1\\\\) means that the \\\\(j^{th}\\\\) subject generated the \\\\(i^{th}\\\\) sample, \\\\(y_{i,j}=0\\\\) means that they did not).

", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Submission Instructions", "content": "

Submit a comma-separated value file containing predicted probabilities, with element (i,j) containing the probability that subject j generated sample i.  The rows are normalized to sum to one.  Refer to the data page for examples of the submission format

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