Computer Science > Computer Vision and Pattern Recognition
arXiv:2307.13428 (cs)
[Submitted on 25 Jul 2023]
Title:An Explainable Model-Agnostic Algorithm for CNN-based Biometrics Verification
View a PDF of the paper titled An Explainable Model-Agnostic Algorithm for CNN-based Biometrics Verification, by Fernando Alonso-Fernandez and 4 other authors
View PDFAbstract:This paper describes an adaptation of the Local Interpretable Model-Agnostic Explanations (LIME) AI method to operate under a biometric verification setting. LIME was initially proposed for networks with the same output classes used for training, and it employs the softmax probability to determine which regions of the image contribute the most to classification. However, in a verification setting, the classes to be recognized have not been seen during training. In addition, instead of using the softmax output, face descriptors are usually obtained from a layer before the classification layer. The model is adapted to achieve explainability via cosine similarity between feature vectors of perturbated versions of the input image. The method is showcased for face biometrics with two CNN models based on MobileNetv2 and ResNet50.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2307.13428 [cs.CV] |
| (orarXiv:2307.13428v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2307.13428 arXiv-issued DOI via DataCite |
Submission history
From: Fernando Alonso-Fernandez [view email][v1] Tue, 25 Jul 2023 11:51:14 UTC (29,979 KB)
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View a PDF of the paper titled An Explainable Model-Agnostic Algorithm for CNN-based Biometrics Verification, by Fernando Alonso-Fernandez and 4 other authors
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