文件列表:

BF_LGBPHS_extraction_FERET.py (7843, 2017-11-14)
computeScores.py (5046, 2017-11-14)
hda-license.pdf (547482, 2017-11-14)

# BTP based on Bloom filters for facial images Biometric Template Protection based on Bloom filters for facial images. Scripts designed for the FERET DB (), following the protocol included in [[IF17]](http://www.sciencedirect.com/science/article/pii/S1566253516301233). ## License This work is licensed under license agreement provided by Hochschule Darmstadt ([h_da-License](/hda-license.pdf)). ## Instructions ### Dependencies * bob.io.base * bob.bio.face * numpy * scipy.op * PIL * math * argparser * os ### Usage 1. Install bob packages () 2. Run BF_LGBPHS_extraction_FERET.py to extract both unprotected and protected templates from the 'fa', 'fb' and 'dup1' partitions of FERET ```python usage: BF_LGBPHS_extraction_FERET.py [-h] [--DBdir_png [DBDIR_PNG]] [--DBtemplates [DBTEMPLATES]] [--DB_BFtemplates [DB_BFTEMPLATES]] [--grayDB [GRAYDB]] [--tanDB [TANDB]] DBdir Extract unprotected LGBPHS and protected Bloom filter templates from the FERET DB. positional arguments: DBdir directory where the compressed face DB is stored optional arguments: -h, --help show this help message and exit --DBdir_png [DBDIR_PNG] directory where the uncompressed face DB will be stored --DBtemplates [DBTEMPLATES] directory where the unprotected face templates will be stored --DB_BFtemplates [DB_BFTEMPLATES] directory where the protected BF face templates will be stored --grayDB [GRAYDB] directory where the intermediate face gray images will be stored (for debug) --tanDB [TANDB] directory where the intermediate face Tan-Triggs processed images will be stored (for debug) ``` 1. Input: folders containing the FERET DB and where to store the templates as well as some intermediate information (arguments can be modified at the top of the script to use other folders) 2. Output: extracted templates and intermediate results (for debugging purposes) 3. Other parameters for the LGBPHS and Bloom filter template extraction might be changed at the top of the script. The values used in [[IF18]] are included as default. 3. Run computeScores.py to compute the mated and non-mated scores ```python usage: computeScores.py [-h] [--scoresDir [SCORESDIR]] DBtemplates DB_BFtemplates Compute unprotected LGBPHS and protected Bloom filter scores from the FERET DB. positional arguments: DBtemplates directory where the unprotected LGBPHS templates are stored DB_BFtemplates directory where the protected BF templates are stored partitionsDir directory where the FERET partition files are stored optional arguments: -h, --help show this help message and exit --scoresDir [SCORESDIR] directory where unprotected and protected scores will be stored ``` 1. Input: folders with the unprotected and protected templates (arguments can be modified at the top of the script to use other folders), as well as the folder where the scores will be stored 2. Output: mated and non-mated scores, stored in text files with one score per row ## References More details in: - [[IS16]](http://www.sciencedirect.com/science/article/pii/S0020025516304753) M. Gomez-Barrero, C. Rathgeb, J. Galbally, C. Busch, J. Fierrez, "Unlinkable and Irreversible Biometric Template Protection based on Bloom Filters", in Elsevier Information Sciences, vol. 370-371, pp. 18-32, 2016 - [[IF18]](http://www.sciencedirect.com/science/article/pii/S1566253516301233) M. Gomez-Barrero, C. Rathgeb, G. Li, R. Raghavendra, J. Galbally and C. Busch, "Multi-Biometric Template Protection Based on Bloom Filters", in Information Fusion, vol. 42, pp. 37-50, 2018. Please remember to reference articles [IS16] and [IF18] on any work made public, whatever the form, based directly or indirectly on these scripts.

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