MOTIVATION: Rare diseases affect millions of people worldwide, yet their genomic foundations remain poorly understood due to limited patient data and strict privacy regulations, such as the General Data Protection Regulation (GDPR) (https://gdpr.eu/tag/gdpr/) in March 2025. These restrictions can hinder the collaborative analysis of genomic data necessary for uncovering disease-causing variants. RESULTS: We present PRISM, a novel privacy-preserving framework based on fully homomorphic encryption (FHE) that facilitates rare disease variant analysis across multiple institutions without exposing sensitive genomic information. To address the challenges of centralized trust, PRISM is built upon a Threshold FHE scheme. This approach decentralizes key management across participating institutions and ensures no single entity can unilaterally decrypt sensitive data. Our method filters disease-causing variants under recessive, dominant, and de novo inheritance models entirely on encrypted data. We propose two algorithmic variants: a multiplication-intensive (MUL-IN) approach and an addition-intensive (ADD-IN) approach. The ADD-IN algorithms minimize the number of costly multiplication operations, enabling up to a 17× improvement in runtime for recessive/dominant filtering and 22× for de novo filtering, compared to MUL-IN methods. While ADD-IN produces larger ciphertexts, efficient parallelization via SIMD and multithreading allows it to handle millions of variants in reasonable time. To the best of our knowledge, this is the first study that utilizes FHE for privacy-preserving rare disease analysis across multiple inheritance models, demonstrating its practicality and scalability in a single-cloud setting. AVAILABILITY AND IMPLEMENTATION: The source code and the data used in this work can be found in https://github.com/mdppml/PRISM.git.

With its wide range of applicability, gender classification is an important task in face image analysis and it has drawn a great interest from the pattern recognition community. In this paper, we aim to deal with this problem using Local Binary Pattern Histogram Sequences as feature vectors in general. Differently from what has been done in similar studies, the algorithm parameters used in cropping and feature extraction steps are selected after an extensive grid search using BANCA and MOBIO databases. The final system which is evaluated on FERET, MORPH-II and LFW with gender balanced and imbalanced training sets is shown to achieve commensurate and better results compared to other state-of-the-art performances on those databases. The system is additionally tested for cross-database training in order to assess its accuracy in real world conditions. For LFW and MORPH-II, BeFIT protocols are used.

One of the most critical sources of variation in face recognition is facial expressions, especially in the frequent case where only a single sample per person is available for enrollment. Methods that improve the accuracy in the presence of such variations are still required for a reliable authentication system. In this paper, we address this problem with an analysis-by-synthesis-based scheme, in which a number of synthetic face images with different expressions are produced. For this purpose, an animatable 3D model is generated for each user based on 17 automatically located landmark points. The contribution of these additional images in terms of the recognition performance is evaluated with three different techniques (principal component analysis, linear discriminant analysis, and local binary patterns) on face recognition grand challenge and Bosphorus 3D face databases. Significant improvements are achieved in face recognition accuracies, for each database and algorithm.

Spoofing is the act of masquerading as a valid user by falsifying data to gain an illegitimate access. Vulnerability of recognition systems to spoofing attacks (presentation attacks) is still an open security issue in biometrics domain and among all biometric traits, face is exposed to the most serious threat, since it is particularly easy to access and reproduce. In this paper, many different types of face spoofing attacks have been examined and various algorithms have been proposed to detect them. Mainly focusing on 2D attacks forged by displaying printed photos or replaying recorded videos on mobile devices, a significant portion of these studies ground their arguments on the flatness of the spoofing material in front of the sensor. However, with the advancements in 3D reconstruction and printing technologies, this assumption can no longer be maintained. In this paper, we aim to inspect the spoofing potential of subject-specific 3D facial masks for different recognition systems and address the detection problem of this more complex attack type. In order to assess the spoofing performance of 3D masks against 2D, 2.5D, and 3D face recognition and to analyze various texture-based countermeasures using both 2D and 2.5D data, a parallel study with comprehensive experiments is performed on two data sets: the Morpho database which is not publicly available and the newly distributed 3D mask attack database.

3D shape data for face recognition is advantageous to its 2D counterpart for being invariant to illumination and pose. However, expression variations and occlusions still remain as major challenges since the shape distortions hinder accurate matching. Numerous algorithms developed to overcome this problem mainly propose region-based approaches, where similarity scores are calculated separately by local regional matchers and fused for recognition. In this paper, we present a regional confidence score assessment scheme that estimates the expression or occlusion induced distortions in different facial regions. Thereby, reliability scores are obtained which can be used in fusion step for recognition. For 7 regions of face, primitive shape distributions are extracted and the surface quality is measured automatically by an Iterative Closest Point (ICP) based method. Using these measurements, an Artificial Neural Network (ANN) is trained and utilized to estimate regional reliability scores. Experiments have been conducted on FRGC v2 3D face database and results demonstrate a high accuracy in surface quality estimation.

Information fusion in biometrics mostly relates to multi-biometric systems which attempt to improve the performance of individual matchers: multi-sensor, multi-algorithm, multimodal, etc. However, in addition to these scenarios, the need for methods to fuse the individual regional classifiers has also emerged, due to the increasing number of region-based methods proposed to overcome expression and occlusion problems in face recognition. In this paper, we present a combination approach by converting the regional match scores into probabilities with the help of estimated regional confidence measures. Initially, face is broken into several segments and similarity and confidence scores are obtained. Then, the posteriori probabilities of the user being genuine are calculated in each region given these two scores. For this calculation, the conditional densities are obtained on the training samples by applying non-parametric kernel density estimation separately for different intervals of confidence levels. Experimental results demonstrate that the inclusion of the regional confidence measures via probabilistic conversion is much more advantageous when compared to weighted sum of original scores.

Numerous major challenges in face recognition, such as pose, illumination, expression and aging, have been investigated extensively. All those variations modify the texture and/or the shape of the face in a similar manner for different individuals. However, studies on alterations applied on face via plastic surgery or prosthetic make-up which can be in countless different ways and amounts, are still very limited. In this paper, we analyze how such changes on nose region affect the face recognition performances of several key techniques. For this purpose, a simulated face database is prepared using FRGC v1.0 in which nose in each sample is replaced with another randomly chosen one. Since this is a 3D database, the impact analysis is not limited to only 2D, which is one of the novelties of this study. Performance comparisons of three 2D and four 3D algorithms are provided. In addition, differently from previous works, baseline results for the original database are also reported. Hence, the impact which is purely due to the applied nose alterations can be measured. The experimental results indicate that with the introduction of alterations both modalities lose precision, especially 3D.

This work studies eye color as a soft biometric trait and provides a novel insight about the influence of pertinent factors in this context, like color spaces, illumination and presence of glasses. A motivation for the paper is the fact that the human iris color is an essential facial trait for Caucasians, which can be employed in iris pattern recognition systems for pruning the search or in soft biometrics systems for person re-identification. Towards studying iris color as a soft biometric trait, we consider a system for automatic detection of eye color, based on standard facial images. The system entails automatic iris localization, followed by classification based on Gaussian Mixture Models with Expectation Maximization. We finally provide related detection results on the UBIRIS2 database employable in a real time eye color detection system.