Resources

Here you can find some useful resources created during our research work.

DISCLAIMER: Any code available from this side is provided “as is” without warranity of any kind. Since the software was written by different members of LUKS, please have a look at the license under which specific software is made avilable. 

 

Software

The Matlab INFace toolbox v2.1coverpng

The INFace (Illumination Normalization techniques for robust Face recognition) toolbox is a collection of Matlab functions and scripts intended to help researchers working in the filed of face recognition. The toolbox was produced as a byproduct of the research work presented here. It includes implementations of several state-of-the-art photometric normalization techniques as well as a number of histogram manipulation functions, which can be useful for the task of illumination invariant face recognition.

Available from: INFace homepage, Matlab central
Documentation: PDF

 4e11929a3237dd3185e1b482cecebe2e386e90c0The PhD face recognition toolbox

The PhD (Pretty helpful Development functions for) face recognition toolbox is a collection of Matlab functions and scripts intended to help researchers working in the filed of face recognition. The toolbox was produced as a byproduct of the research work presented here and here. It includes implementations of several state-of-the-art face recognition techniques as well as a number of demo scripts, which can be extremely useful for beginners in the field of face recognition.

Available from: PhD toolbox homepage, Matlab central
Documentation: PDF

aweAnnotated Web Ears (AWE) toolbox and dataset

Annotated Web Ears (AWE) is a dataset of ear images gathered from the web and in the current form contains 1000 ear images of 100 distinct subjects. The dataset was collected for the goal of studying unconstrained ear recognition and is made publicly available to the community. The dataset comes with a Matlab toolbox dedicated to research in ear recognition. The AWE toolbox implements several state-of-the-art ear recognition techniques and allows for rapid experimentation with the AWE dataset.

Available from: AWE homepage