Publications

@article{apap_explainable_2024,

title = {Explainable multi-layer COSFIRE filters robust to corruptions and boundary attack with application to retina and palmprint biometrics},

author = {Adrian Apap and Amey Bhole and Laura Fernández-Robles and Manuel Castejón-Limas and George Azzopardi},

url = {https://link.springer.com/article/10.1007/s00521-024-10164-8},

year  = {2024},

date = {2024-01-01},

journal = {Neural Computing and Applications},

volume = {36},

number = {30},

pages = {19231–19245},

abstract = {This work introduces a unified and explainable biometric recognition method using hierarchical COSFIRE filters for retina and palmprint identification. The approach leverages COSFIRE’s one-shot trainable nature to capture spatial arrangements of keypoints in biometric images. It delivers state-of-the-art accuracy, including perfect classification on retina datasets and 97.54% on a palmprint dataset. The system remains robust against partial data and most adversarial attacks, while also offering clear, interpretable decision processes suitable for broader applications.},

note = {Publisher: Springer London London},

keywords = {biometrics, COSFIRE Filters, Explanaible AI},

pubstate = {published},

tppubtype = {article}

}

@article{apap_retinal_2018,

title = {Retinal Fundus Biometric Analysis using COSFIRE Filters},

author = {Adrian Apap and Laura Fernández-Robles and George Azzopardi},

url = {https://scholar.google.es/citations?view_op=view_citation&hl=en&user=4jZgNVkAAAAJ&cstart=20&pagesize=80&sortby=title&citation_for_view=4jZgNVkAAAAJ:WA5NYHcadZ8C},

year  = {2018},

date = {2018-01-01},

journal = {Proceedings of the first international APPIS conference, Gran Canaria, Spain},

abstract = {Proceedings of the first international APPIS conference},

keywords = {Biometric Recognition, COSFIRE Filters, Feature extraction, Retinal Biometrics},

pubstate = {published},

tppubtype = {article}

}

@article{azzopardi_increased_2016,

title = {Increased generalization capability of trainable cosfire filters with application to machine vision},

author = {George Azzopardi and Laura Fernández-Robles and Enrique Alegre and Nicolai Petkov},

url = {https://ieeexplore.ieee.org/abstract/document/7900152},

year  = {2016},

date = {2016-01-01},

journal = {2016 23rd International Conference on Pattern Recognition (ICPR)},

pages = {3356–3361},

abstract = {This paper proposes an optimization for COSFIRE filters using a genetic algorithm to select the best contour parts for pattern recognition. The optimized COSFIRE filters, tested on a dataset of milling machine images, showed improved precision (P = 91.99%) and recall (R = 96.22%) compared to the original version, enhancing efficiency and generalization while maintaining selectivity.},

note = {Publisher: IEEE},

keywords = {COSFIRE Filters, Genetic Algorithm, Image classification, Pattern Recognition},

pubstate = {published},

tppubtype = {article}

}