Raul Ismayilov

Willams de Lima Costa, Raul Ismayilov, Nicola Strisciuglio et al.

The classification of indoor scenes is a critical component in various applications, such as intelligent robotics for assistive living. While deep learning has significantly advanced this field, models often suffer from reduced performance due to image corruption. This paper presents an innovative approach to indoor scene recognition that leverages multimodal data fusion, integrating caption-based semantic features with visual data to enhance both accuracy and robustness against corruption. We examine two multimodal networks that synergize visual features from CNN models with semantic captions via a Graph Convolutional Network (GCN). Our study shows that this fusion markedly improves model performance, with notable gains in Top-1 accuracy when evaluated against a corrupted subset of the Places365 dataset. Moreover, while standalone visual models displayed high accuracy on uncorrupted images, their performance deteriorated significantly with increased corruption severity. Conversely, the multimodal models demonstrated improved accuracy in clean conditions and substantial robustness to a range of image corruptions. These results highlight the efficacy of incorporating high-level contextual information through captions, suggesting a promising direction for enhancing the resilience of classification systems.

Raul Ismayilov, Luuk Spreeuwers

Face morphing attacks compromise biometric security by creating document images that verify against multiple identities, posing significant risks from document issuance to border control. Differential Morphing Attack Detection (D-MAD) offers an effective countermeasure, particularly when employing face demorphing to disentangle identities blended in the morph. However, existing methods lack operational generalizability due to limited training data and the assumption that all document inputs are morphs. This paper presents SFDemorpher, a framework designed for the operational deployment of face demorphing for D-MAD that performs identity disentanglement within joint StyleGAN latent and high-dimensional feature spaces. We introduce a dual-pass training strategy handling both morphed and bona fide documents, leveraging a hybrid corpus with predominantly synthetic identities to enhance robustness against unseen distributions. Extensive evaluation confirms state-of-the-art generalizability across unseen identities, diverse capture conditions, and 13 morphing techniques, spanning both border verification and the challenging document enrollment stage. Our framework achieves superior D-MAD performance by widening the margin between the score distributions of bona fide and morphed samples while providing high-fidelity visual reconstructions facilitating explainability.

Raul Ismayilov, Dzemila Sero, Luuk Spreeuwers

Synthetic face datasets are increasingly used to overcome the limitations of real-world biometric data, including privacy concerns, demographic imbalance, and high collection costs. However, many existing methods lack fine-grained control over identity attributes and fail to produce paired, identity-consistent images under structured capture conditions. We introduce FLUXSynID, a framework for generating high-resolution synthetic face datasets along with a dataset of 14,889 synthetic identities. We generate synthetic faces with user-defined identity attribute distributions, offering both document-style and trusted live capture images. The dataset generated using the FLUXSynID framework shows improved alignment with real-world identity distributions and greater inter-class diversity compared to prior work. Our work is publicly released to support biometric research, including face recognition and morphing attack detection.