Objave

@inproceedings{Diffiqa_2023,

title = {DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic Models},

author = {Žiga Babnik and Peter Peer and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2023/09/121.pdf

https://lmi.fe.uni-lj.si/wp-content/uploads/2023/09/121-supp.pdf},

year  = {2023},

date = {2023-09-01},

booktitle = {IEEE International Joint Conference on Biometrics },

pages = {1-10},

publisher = {IEEE},

address = {Ljubljana, Slovenia},

abstract = {Modern face recognition (FR) models excel in constrained

scenarios, but often suffer from decreased performance

when deployed in unconstrained (real-world) environments

due to uncertainties surrounding the quality

of the captured facial data. Face image quality assessment

(FIQA) techniques aim to mitigate these performance

degradations by providing FR models with sample-quality

predictions that can be used to reject low-quality samples

and reduce false match errors. However, despite steady improvements,

ensuring reliable quality estimates across facial

images with diverse characteristics remains challenging.

In this paper, we present a powerful new FIQA approach,

named DifFIQA, which relies on denoising diffusion

probabilistic models (DDPM) and ensures highly competitive

results. The main idea behind the approach is to utilize

the forward and backward processes of DDPMs to perturb

facial images and quantify the impact of these perturbations

on the corresponding image embeddings for quality

prediction. Because the diffusion-based perturbations are

computationally expensive, we also distill the knowledge

encoded in DifFIQA into a regression-based quality predictor,

called DifFIQA(R), that balances performance and

execution time. We evaluate both models in comprehensive

experiments on 7 diverse datasets, with 4 target FR models

and against 10 state-of-the-art FIQA techniques with

highly encouraging results. The source code is available

from: https://github.com/LSIbabnikz/DifFIQA.},

keywords = {biometrics, deep learning, denoising diffusion probabilistic models, diffusion, face, face image quality assesment, face recognition, FIQA, quality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{MarijaTomato2023,

title = {TomatoDIFF: On–plant Tomato Segmentation with Denoising Diffusion Models},

author = {Marija Ivanovska and Vitomir Štruc and Janez Perš },

url = {https://arxiv.org/pdf/2307.01064.pdf

https://ieeexplore.ieee.org/document/10215774},

doi = {10.23919/MVA57639.2023.10215774},

year  = {2023},

date = {2023-07-23},

urldate = {2023-07-23},

booktitle = {18th International Conference on Machine Vision and Applications (MVA 2023)},

pages = {1-6},

abstract = {Artificial intelligence applications enable farmers to optimize crop growth and production while reducing costs and environmental impact. Computer vision-based algorithms in particular, are commonly used for fruit segmentation, enabling in-depth analysis of the harvest quality and accurate yield estimation. In this paper, we propose TomatoDIFF, a novel diffusion-based model for semantic segmentation of on-plant tomatoes. When evaluated against other competitive methods, our model demonstrates

state-of-the-art (SOTA) performance, even in challenging environments with highly occluded fruits. Additionally, we introduce Tomatopia, a new, large and challenging dataset of greenhouse tomatoes. The dataset comprises high-resolution RGB-D images and pixel-level annotations of the fruits. The source code of TomatoDIFF and Tomatopia are available at https://github. com/MIvanovska/TomatoDIFF},

keywords = {agriculture, dataset, deep learning, diffusion, plan segmentation, plant monitoring, robotics, segmentation, tomato dataset},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{IWBF2023_Marija,

title = {Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models},

author = {Marija Ivanovska and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2023/03/IWBF2023_Morphing.pdf},

year  = {2023},

date = {2023-02-28},

booktitle = {Proceedings of the International Workshop on Biometrics and Forensics (IWBF)},

pages = {1-6},

abstract = {Morphed face images have recently become a growing concern for existing face verification systems, as they are relatively easy to generate and can be used to impersonate someone's identity for various malicious purposes. Efficient Morphing Attack Detection (MAD) that generalizes well across different morphing techniques is, therefore, of paramount importance. Existing MAD techniques predominantly rely on discriminative models that learn from examples of bona fide and morphed images and, as a result, often exhibit sub-optimal generalization performance when confronted with unknown types of morphing attacks. To address this problem, we propose a novel, diffusion--based MAD method in this paper that learns only from the characteristics of bona fide images. Various forms of morphing attacks are then detected by our model as out-of-distribution samples. We perform rigorous experiments over four different datasets (CASIA-WebFace, FRLL-Morphs, FERET-Morphs and FRGC-Morphs) and compare the proposed solution to both discriminatively-trained and once-class MAD models. The experimental results show that our MAD model achieves highly competitive results on all considered datasets.},

keywords = {biometrics, deep learning, denoising diffusion probabilistic models, diffusion, face, face morphing attack, morphing attack, morphing attack detection},

pubstate = {published},

tppubtype = {inproceedings}

}