Facial image-based age and gender classification is a foundational problem in computer vision, yet performance is often limited by data scarcity and privacy constraints. This paper proposes IDiff-Face-Aged, a novel age transformation framework that utilizes multimodal embeddings to generate realistic and diverse facial images. Two synthetic datasets, Fading (utilizing null text-inversion prompting) and IDiff-Face-Aged1, were tested across various architectures, including MultiEfficientNet, MultiLightViT, and MultiMobileNet. Three age estimation methods were used to address both continuous and categorical intervals. Experimental results indicate that synthetic data contributes to improved model accuracy, especially by enhancing representation in under-sampled age groups. On the UTKFace dataset, an accuracy gain of over $3 \%$ was observed. Moreover, models trained with a mix of synthetic and real data show stronger generalization capabilities, particularly when datasets design and alignment are carefully managed. These findings illustrate the potential of synthetic data to supplement real-world datasets, while also pointing to ongoing challenges related to data realism and artifact avoidance.1Method under Patent Pending Process

Adversarial attacks pose a significant threat to the reliability of biometric systems, particularly in security-critical applications such as identity verification and access control. Ensuring robustness against such attacks is essential for the safe deployment of face recognition technologies in real-world scenarios. To advance this goal, the 2025 Adversarial Attack Challenge for Secure Face Recognition was organized as part of the International Joint Conference on Biometrics (IJCB) 2025.The competition focused on two main tracks: Detection, where the objective was to determine whether a given face image is clean or adversarial, and Resilience, which aimed to evaluate recognition systems under adversarial perturbations. Participants were provided with a standardized dataset derived from CelebA and LFW, encompassing both clean samples and adversarial images crafted using ten diverse attack methods targeting evasion and impersonation scenarios. To ensure fairness and reproducibility, all models were trained solely on the data provided, with support from a custom open source adversarial attack package tailored for face recognition.In addition to benchmarking adversarial robustness, the challenge contributes to the research community by releasing the data set and the extensible attack package, allowing further investigation of secure and reliable face recognition systems.

This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 In-ternational Joint Conference on Biometrics (IJCB 2022). The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 differ-ent countries. In the end, seven valid submissions were submitted by the participating teams and evaluated by the organizers. The competition was held to present and at-tract solutions that deal with detecting face morphing at-tacks while protecting people's privacy for ethical and le-gal reasons. To ensure this, the training data was limited to synthetic data provided by the organizers. The submitted solutions presented innovations that led to out-performing the considered baseline in many experimental settings. The evaluation benchmark is now available at: https://github.com/marcohuber/SYN-MAD-2022.

Odometry consists in using data from a moving sensor to estimate change in position over time. It is a crucial step for several applications in robotics and computer vision. This paper presents a novel approach for estimating the relative motion between suc-cessive RGB-D frames that uses plane-primitives instead of point features. The planes in the scene are extracted and the motion estimation is cast as a plane-to-plane registra-tion problem with a closed-form solution. Point features are only extracted in the cases where the plane surface configuration is insufficient to determine motion with no ambi-guity. The initial estimate is refined in a photo-geometric optimization step that takes full advantage of the plane detection and simultaneous availability of depth and visual appearance cues. Extensive experiments show that our plane-based approach is as accu-rate as state-of-the-art point-based approaches when the camera displacement is small, and significantly outperforms them in case of wide-baseline and/or dynamic foreground. 1

Recovering the epipolar geometry of a stereo image pair is important for many computer and robotic vision systems, for performing motion recovering, 3D reconstruction and, more recently, image retrieval from large databases. Most state-of-the-art methods for estimating the fundamental matrix rely solely in putative image correspondences, and, therefore, heavily depend on the capability of the low-level image features to provide enough distinctiveness capabilities for establishing correct matches. In this paper we present a robust method for estimating the fundamental matrix based on all image features, and not only matching points. This is done by selecting the best correspondent keypoints between views through a proper weighting function that fuses local appearance of keypoints and distance to the epipolar lines. Several distance weighting functions are compared, with an intuitive theoretical analysis of the role of each function parametrization being analyzed. Experimental evidence shows that our approach outperforms the current state-of-the-art methods in terms of error magnitude, number of correct matches provided and computational time.

This article addresses the problem of imagebased localization in indoor environments. The localization is achieved by querying a database of omnidirectional images that constitutes a detailed visual map of the building where the robot operates. Omnidirectional cameras have the advantage, when compared to standard perspectives, of capturing in a single frame the entire visual content of a room. This, not only speeds up the process of acquiring data for creating the map, but also favors scalability by significantly decreasing the size of the database. The problem is that omnidirectional images have strong non-linear distortion, which leads to poor retrieval results when the query images are standard perspectives. This paper reports for the first time thorough experiments in using perspectives to index a database of para-catadioptric images for the purpose of robot localization. We propose modifications to the SIFT algorithm that significantly improve point matching between the two types of images with positive impact in the recognition based in visual words. We also compare the classical bags-of-words against the recent framework of visual-phrases, showing that the latter outperforms the former.

Keypoint detection and matching is of fundamental importance for many applications in computer and robot vision. The association of points across different views is problematic because image features can undergo significant changes in appearance. Unfortunately, state-of-the-art methods, like the scale-invariant feature transform (SIFT), are not resilient to the radial distortion that often arises in images acquired by cameras with microlenses and/or wide field-of-view. This paper proposes modifications to the SIFT algorithm that substantially improve the repeatability of detection and effectiveness of matching under radial distortion, while preserving the original invariance to scale and rotation. The scale-space representation of the image is obtained using adaptive filtering that compensates the local distortion, and the keypoint description is carried after implicit image gradient correction. Unlike competing methods, our approach avoids image resampling (the processing is carried out in the original image plane), it does not require accurate camera calibration (an approximate modeling of the distortion is sufficient), and it adds minimal computational overhead. Extensive experiments show the advantages of our method in establishing point correspondence across images with radial distortion.

Image keypoints are broadly used in robotics for different purposes, ranging from recognition to 3D reconstruction, passing by SLAM and visual servoing. Robust keypoint matching across different views is problematic because of the relative motion between camera and scene that causes significant changes in feature appearance. The problem can be partially overcome by using state-of-the-art methods for keypoint detection and matching, that are resilient to common affine transformations such as changes in scale and rotation. Unfortunately, these approaches are not invariant to the radial distortion present in images acquired by cameras with wide field-of-view. This article proposes modifications to the Scale Invariant Feature Transform (SIFT), that improve the repeatability of detection and effectiveness of matching in the presence of distortion, while preserving the characteristics of invariance to scale and rotation. These modifications require an approximate modeling of the image distortion, and consist in using adaptative gaussian filtering for detection and implicit gradient correction for description. Extensive experiments, with both synthetic and real images, show that our method outperforms explicit distortion correction using image rectification.