Code & Data








Code & Data





Multichannel audio-visual database including recordings in driving conditions. The database is free and includes manual annotations for in-studio images under various head poses




Manual annotations for first 100 scans of FRGC database



If using these annotations please cite the following work, where we also analyze the consistency of the new annotations in relation to previously available ones:


·        F.M. Sukno, J.L. Waddington and P.F. Whelan. Compensating inaccurate annotations to train 3D facial landmark localization models. In Proc. FG Workshop on 3D Face Biometrics, Shanghai, China, 2013.


Details about the dataset


Manual annotations





SRILF 3D Face Landmarker



Free 3D face landmarking software (Windows binaries). The goal of this command-line application is to locate facial landmarks on a 3D scan in a fully automatic manner. The implementation, based on the SRiLF algorithm, uses shape and descriptor statistics which are encoded in a configuration model that controls the behavior of the landmaker.


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Download SRILF 3D Face Landmarker v1.0

User guide of the landmarker

Description of the SRILF algorithm

Some examples of landmark localization using SRILF v1.0:

More examples of SRILF (on FRGC and Bosphorus databases)

Demonstration video of scanning (with Hand-held laser device) and landmarking





Configuration models for SRILF 3D Face Landmarker



The configuration model allows controlling several aspects of the SRILF 3D Face Landmarker software, including which (and how many) landmarks are targeted and the expected type of input surface.

The configuration model srilf3dFL_HHLaser_HQ.cfg is provided with the software by default. It has been created from a training set of facial surfaces scanned with a hand-held laser device, with a resolution between 1mm and 2mm, with no significant holes or missing parts.The subjects that were scanned were mainly Western-European, had their eyes closed (because of laser safety) and were instructed to pose with neutral facial expression.

For optimal performance, input surfaces should meet the above specifications. In case the input surfaces do not meet the requirements of the provided model, it is recommended to either: i) Pre-process the input surfaces appropriately (if possible); or ii) Contact us to check if there is a more suitable model for your needs. Models for the FRGC and Bosphorus databases are also available.


Default model: Congiuration model trained on Hand-held laser data, targeting 12 facial landmarks; see User Guide for details.

FRGCv1 model: Configuration model targeting 14 landmarks, trained with facial scans from the FRGCv1 database. See video examples here.

FRGCv2 model: Configuration model targeting 14 landmarks, trained with facial scans from the FRGCv2 database. See video examples here.

Bosphorus model: Configuration model targeting 14 landmarks, trained with facial scans from the Bosphorus database. See snapshot examples here.