Modern face recognition (FR) models, particularly their convolutional neural network based implementations, often raise concerns regarding privacy and ethics due to their "black-box" nature. To enhance the explainability of FR models and the interpretability of their embedding space, we introduce in this paper three novel techniques for discovering semantically meaningful feature directions (or axes). The first technique uses a dedicated facial-region blending procedure together with principal component analysis to discover embedding space direction that correspond to spatially isolated semantic face areas, providing a new perspective on facial feature interpretation. The other two proposed techniques exploit attribute labels to discern feature directions that correspond to intra-identity variations, such as pose, illumination angle, and expression, but do so either through a cluster analysis or a dedicated regression procedure. To validate the capabilities of the developed techniques, we utilize a powerful template decoder that inverts the image embedding back into the pixel space. Using the decoder, we visualize linear movements along the discovered directions, enabling a clearer understanding of the internal representations within face recognition models. The source code will be made publicly available.

The need for reliable identification of children in various emerging applications has sparked interest in leveraging child face recognition technology. This study introduces a longitudinal approach to enrollment and verification accuracy for child face recognition, focusing on the YFA (Young Face Aging) database collected by Clarkson University’s CITeR research group over an 8-year period, at 6-month intervals. The dataset includes children ranging from 3 to 18 years of age, comprising 330 subjects with an average of 6 data collections per subject. Our research aims to comprehensively evaluate the performance of state-of-the- art face-matching techniques on the YFA database, assessing the feasibility of recognizing children's faces upon initial enrollment and verifying their identity longitudinally at 6-month intervals. We conduct a comprehensive analysis of the system’s accuracy considering multiple age groups. We also investigate the temporal degradation of face recognition accuracy over time. Notably, when comparing the initial enrollment image with longitudinal images over an 8-year period, we observe a decrease in accuracy. The average TAR across all age groups is 98.52% with a FAR of 0.1% with a 2-year age verification gap and drops to 95.68 with a 4-year age gap. However, this rate decreases to 87.24% after a time difference of 6 years and further drops to 71.32% with a time difference of 8 years. The highest drop in accuracy was noticed in the age group of (3-5) years old children and the lowest in (5.5-7) years old. By addressing the challenges and opportunities in child face recognition, this research contributes significantly to the advancement of technology for identifying missing or abducted children and other critical applications requiring dependable biometric recognition in children.

Liveness Detection (LivDet)-Face is an international competition series open to academia and industry. The competition’s objective is to assess and report state-of-the-art in liveness / Presentation Attack Detection (PAD) for face recognition. Impersonation and presentation of false samples to the sensors can be classified as presentation attacks and the ability for the sensors to detect such attempts is known as PAD. LivDet-Face 2021 * will be the first edition of the face liveness competition. This competition serves as an important benchmark in face presentation attack detection, offering (a) an independent assessment of the current state of the art in face PAD, and (b) a common evaluation protocol, availability of Presentation Attack Instruments (PAI) and live face image dataset through the Biometric Evaluation and Testing (BEAT) platform. The competition can be easily followed by researchers after it is closed, in a platform in which participants can compare their solutions against the LivDet-Face winners.

In this work, we utilize progressive growth-based Generative Adversarial Networks (GANs) to develop the Clarkson Fingerprint Generator (CFG). We demonstrate that the CFG is capable of generating realistic, high fidelity, $512 \times 512$ pixels, full, plain impression fingerprints. Our results suggest that the fingerprints generated by the CFG are unique, diverse, and resemble the training dataset in terms of minutiae configuration and quality, while not revealing the underlying identities of the training data. We make the pre-trained CFG model and the synthetically generated dataset publicly available at https://github.com/keivanB/Clarkson_Finger_Gen