INface Toolbox

The INFace (Illumination Normalization techniques for robust Face recognition) toolbox is a collection of Matlab functions intended for researchers working in the field of face recognition. The toolbox was produced as a byproduct of the research work presented here and is freely available for download.

The INface toolbox v2.0 includes implementations of the following photometric normalization techniques: the single-scale-retinex algorithm, the multi-scale-retinex algorithm, the single-scale self quotient image, the multi-scale self quotient image, the homomorphic-filtering-based normalization technique, a wavelet-based normalization technique, a wevelet-denoising-based normalization technique, the isotropic diffusion-based normalization technique, the anisotropic-diffusion-based normalization technique, the non-local means-based normalization technique, the adaptive non-local-means-based normalization technique, the DCT-based normalization technique, a normalization technique based on steerable filters, a modified version of the anisotropic diffusion-based normalization technique, the Gradientfaces approach, the Weberfaces approach, the multi-scale Weberfaces approach, the Tan and Triggs normalization technique and the large and small scale features normalization technique.

In addition to the implementations of several state-of-the-art photometric normalization techniques, it also includes a number of histogram manipulation functions, which can be useful for the task of illumination invariant face recognition.


Browse through the INFace v2.1 manual HERE.


Download the INFace toolbox from:


If you use the INFace toolbox for your research work, please cite the following publications:

title = {Photometric normalization techniques for illumination invariance},
author = {Vitomir Štruc and Nikola Pavešić},
editor = {Yu-Jin Zhang},
doi = {10.4018/978-1-61520-991-0.ch015},
year = {2011},
booktitle = {Advances in Face Image Analysis: Techniques and Technologies},
pages = {279-300},
publisher = {IGI-Global},
title = {Strengths and weaknesses of deep learning models for face recognition against image degradations},
author = {Klemen Grm and Vitomir Štruc and Anais Artiges and Matthieu Caron and Hazim K. Ekenel},
url = {},
year = {2017},
date = {2017-01-01},
journal = {IET Biometrics},
volume = {7},
number = {1},
pages = {81--89},
publisher = {IET},