Gestalt Interest Points for Image Description in Weight-Invariant Face Recognition

Abstract

In this work, we propose two improvements of the Gestalt Interest Points (GIP) algorithm for the recognition of faces of people that have underwent signi cant weight change. The basic assumption is that some interest points contribute more to the description of such objects than others. We assume that we can eliminate certain interest points to make the whole method more e cient while retaining our classi cation results. To nd out which gestalt interest points can be eliminated, we did experiments concerning contrast and orientation of face features. Furthermore, we investigated the robustness of GIP against image rotation. The experiments show that our method is rotational invariant and { in this practically relevant forensic domain { outperforms the state-of-the-art methods such as SIFT, SURF, ORB and FREAK.

Reference

M. Hörhan, H. Eidenberger: "Gestalt Interest Points for Image Description in Weight-Invariant Face Recognition"; Talk: SPIE Visual Communications and Image Processing Conference, Paris, FR; 09-17-2014 - 09-20-2014; in: "SPIE Visual Communications Proceedings", SPIE, (2014).

BibTeX

@inproceedings{TUW-233299, author = {H{\"o}rhan, Markus and Eidenberger, Horst}, title = {Gestalt Interest Points for Image Description in Weight-Invariant Face Recognition}, booktitle = {SPIE Visual Communications Proceedings}, year = {2014}, publisher = {SPIE}, keywords = {Face recognition, person identi cation, content-based image analysis, local interest point detection}, note = {talk: SPIE Visual Communications and Image Processing Conference, Paris, FR; 2014-09-17 -- 2014-09-20} } Click into the text area and press Ctrl+A/Ctrl+C or ⌘+A/⌘+C to copy the BibTeX into your clipboard… or download the BibTeX.