An Open Source Framework for Standardized Comparisons of Face Recognition Algorithms
| Type of publication: | Conference paper |
| Citation: | Gunther_BEFIT2012_2012 |
| Publication status: | Published |
| Booktitle: | Computer Vision - ECCV 2012. Workshops and Demonstrations |
| Series: | Lecture Notes in Computer Science |
| Volume: | 7585 |
| Year: | 2012 |
| Month: | October |
| Pages: | 547-556 |
| Publisher: | Springer Berlin |
| Location: | Heidelberg |
| Organization: | Idiap Research Institute |
| Address: | Rue Marconi 19, CH - 1920 Martigny, Switzerland |
| Note: | The source code to re-generate the results of this paper can be downloaded from the URL below |
| ISBN: | 978-3-642-33884-7 |
| Crossref: | Gunther_Idiap-RR-29-2012: |
| URL: | http://pypi.python.org/pypi/xf... |
| DOI: | 10.1007/978-3-642-33885-4_55 |
| Abstract: | In this paper we introduce the facereclib, the first software library that allows to compare a variety of face recognition algorithms on most of the known facial image databases and that permits rapid prototyping of novel ideas and testing of meta-parameters of face recognition algorithms. The facereclib is built on the open source signal processing and machine learning library Bob. It uses well-specified face recognition protocols to ensure that results are comparable and reproducible. We show that the face recognition algorithms implemented in Bob as well as third party face recognition libraries can be used to run face recognition experiments within the framework of the facereclib. As a proof of concept, we execute four different state-of-the-art face recognition algorithms: local Gabor binary pattern histogram sequences (LGBPHS), Gabor graph comparisons with a Gabor phase based similarity measure, inter-session variability modeling (ISV) of DCT block features, and the linear discriminant analysis on two different color channels (LDA-IR) on two different databases: The Good, The Bad, & The Ugly, and the BANCA database, in all cases using their fixed protocols. The results show that there is not one face recognition algorithm that outperforms all others, but rather that the results are strongly dependent on the employed database. |
| Keywords: | Biometrics, Face Recognition, Open Source, Reproducible research |
| Projects |
Idiap FP 7 |
| Authors | |
| Editors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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