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The INface toolbox v2.0 for illumination invariant face recognition

version 1.8.0.0 (715 KB) by Vitomir Struc
Photometric normalization techniques used for achieving illumination invariant face recognition.

14K Downloads

Updated 26 Jan 2012

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The INFace (Illumination Normalization techniques for robust Face recognition) toolbox v2.0 is a collection of Matlab functions and scripts intended to help researchers working in the field of face recognition. The toolbox was produced as a byproduct of my research work 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 listed techniques there are also a number of histogram manipulation functions included in the toolbox, which can be useful for the task of illumination invariant face recognition.

Cite As

Vitomir Struc (2022). The INface toolbox v2.0 for illumination invariant face recognition (https://www.mathworks.com/matlabcentral/fileexchange/26523-the-inface-toolbox-v2-0-for-illumination-invariant-face-recognition), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2007b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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