Face Detection Datasets

The first (of many more) face detection datasets of human faces especially created for face detection (finding) instead of recognition:

  • BioID Face Detection Database
    1521 images with human faces, recorded under natural conditions, i.e. varying illumination and complex background. The eye positions have been set manually (and are included in the set) for calculating the accuracy of a face detector. A formula is presented to normalize the decision of a match or mismatch. This is, to my knowledge, the first attempt to finally create a real test scenario with precise rules on how to calculate the accuracy of a face detector – open for all to compare their results in a scientific way!

    • A complete revision of all eye position files has been released 2/25/02 – visit https://www.bioid.com/facedb/ to update the dataset.
    • The original article describing the database can be downloaded here.
    • For comparison, the data (figure 5 of the article above) of the reference test is now available in RTF format for both the BioID-test and the XM2VTS-test.

    BioID Gesture FGnet face detection datasetsA new addition: The BioID Face Detection Database is being used within the FGnet project of the European Working Group on face and gesture recognition. Therefore, several additional feature points have been marked up, which are very useful for facial analysis and gesture recognition. This data is also available for public download here.


  • Face and Gesture Recognition Working Group FGnet
    FGnet facial recognition European FGnet encourages development of face and gesture recognition techniques. Among other contributions worth having a look at, they provide resources especially useful for face detection/recognition. Have a look at “Benchmark Data” to access the list of useful datasets!


  • WIDER FACE: A Face Detection Benchmark
    The WIDER FACE dataset is a face detection benchmark dataset. It consists of 32.203 images with 393.703 labelled faces with high variations of scale, pose and occlusion.

Many other face databases are available nowadays. The current trend is to recognize faces from different views, under varying illumination, or along time differences (aging).  Here are some especially useful for testing face detection performance:

Sooner or later, you will feel the need for an average face model when trying different locating algorithms. Here are some averaged faces: