Mobile Custom Silicone Mask Attack Dataset (CSMAD-Mobile)

This dataset consists of face & silicon masks images from 8 different subjects captured with 3 different smartphones.

Get Data


This dataset consists of images captured from 8 different bona fide subjects using three different smartphones (iPhone X, Samsung S7 and Samsung S8). For each subject within the database, varying number of samples are collected using all the three phones. Similarly, the silicone masks of each of the subject is collected using three phones. The masks, each costing about USD 4000, have been manufactured by a professional special-effects company.

For the bona fide presentations of the same eight subjects, each data subject is asked to pose in a manner compliant to standard portrait capture. The data is captured indoors, with adequate artificial lighting. Silicone mask presentations have been captured under similar conditions, by placing the masks on their bespoke support provided by the manufacturer, with prosthetic eyes and silicone eye sockets.

The database is organized in three folders corresponding to three smartphones and further each subject within the database is organized in sub-folders.


  • PHONE is iPhone, SamS7 or SamS8 corresponding to iPhone, Samsung S7 and Samsung S8 respectively.
  • CLASS is "Bona" or "Mask" indicating the bona fide presentation or mask presentation respectively.
  • SUBJECTNUMBER is "s1" to "s8" indicating 8 subjects in the database.
  • PHONEIDENTIFIER is the two letter keyword as given by "ip", "s7" and "s8" corresponding to iPhone, Samsung S7 and Samsung S7 respectively.
  • PRESENTATION identifies bona-fide or mask-attack presentation using 2 letter identifier "bp" or "ap".
  • SAMPLENUMBER indicates the sample number of the subject.


If you publish results using this dataset, please cite the following publication.

“Custom Silicone Face Masks - Vulnerability of Commercial Face Recognition Systems & Presentation Attack Detection”, R. Raghavendra, S. Venkatesh, K. B. Raja, S. Bhattacharjee, P. Wasnik, S. Marcel, and C. Busch. IAPR/IEEE International Workshop on Biometrics and Forensics (IWBF), 2019.