P339 - BIOSIG 2023 - Proceedings of the 22nd International Conference of the Biometrics Special Interest Group
Auflistung P339 - BIOSIG 2023 - Proceedings of the 22nd International Conference of the Biometrics Special Interest Group nach Erscheinungsdatum
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- KonferenzbeitragRobust Sclera Segmentation for Skin-tone Agnostic Face Image Quality Assessment(BIOSIG 2023, 2023) Wassim Kabbani, Christoph BuschFace image quality assessment (FIQA) is crucial for obtaining good face recognition performance. FIQA algorithms should be robust and insensitive to demographic factors. The eye sclera has a consistent whitish color in all humans regardless of their age, ethnicity and skin-tone. This work proposes a robust sclera segmentation method that is suitable for face images in the enrolment and the border control face recognition scenarios. It shows how the statistical analysis of the sclera pixels produces features that are invariant to skin-tone, age and ethnicity and thus can be incorporated into FIQA algorithms to make them agnostic to demographic factors.
- KonferenzbeitragUnified Face Image Quality Score based on ISO/IEC Quality Components(BIOSIG 2023, 2023) Praveen Kumar Chandaliya, Kiran RajaFace image quality assessment is crucial in the face enrolment process to obtain high-quality face images in the reference database. Neglecting quality control will adversely impact the accuracy and efficiency of face recognition systems, resulting in an image captured with poor perceptual quality. In this work, we present a holistic combination of $21$ component quality measures proposed in ``ISO/IEC CD 29794-5" and identify the varying nature of different measures across different datasets. The variance is seen across both capture-related and subject-related measures, which can be tedious for validating each component metric by a human observer when judging the quality of the enrolment image. Motivated by this observation, we propose an efficient method of combining quality components into one unified score using a simple supervised learning approach. The proposed approach for predicting face recognition performance based on the obtained unified face image quality assessment (FIQA) score was comprehensively evaluated using three datasets representing diverse quality factors. We extensively evaluate the proposed approach using the Error-vs-Discard Characteristic (EDC) and show its applicability using five different FRS. The evaluation indicates promising results of the proposed approach combining multiple component scores into a unified score for broader application in face image enrolment in FRS.
- KonferenzbeitragTowards Contactless Fingerprint Presentation Attack Detection using Algorithms from the Contact-based Domain(BIOSIG 2023, 2023) Jannis Priesnitz, Roberto CasulaIn this work, we investigate whether contact-based fingerprint Presentation Attack Detection (PAD) methods can generalize to the contactless domain. To this end, we selected a state-of-the-art patch-based fingerprint PAD algorithm which achieved high detection performance in the contact-based domain and adapted it for contactless fingerprints. We train and test the method using three contactless fingerprint databases and evaluate its generalization capabilities using Leave-One-Out (LOO) protocols. Further, we acquired a new PAD database and use it in a cross-database evaluation. The adopted method shows low error rates in most scenarios and can generalize to unseen contactless presentation attacks.
- KonferenzbeitragA RISE-based explainability method for genuine and impostor face verification(BIOSIG 2023, 2023) Naima Bousnina, Joao AscensoHeat Map (HM)-based explainable Face Verification (FV) has the goal to visually interpret the decision-making of black-box FV models. Despite the impressive results, state-of-the-art FV explainability methods based on HMs mainly address genuine verification by generating visual explanations that reveal the similar face regions which most contributed for acceptance decisions. However, the similar face regions may not be the unique critical regions for the model decision, notably when rejection decisions are performed. To address this issue, this paper proposes a more complete FV explainability method, providing meaningful HM-based explanations for both genuine and impostor verification and associated acceptance and rejection decisions. The proposed method adapts the RISE algorithm for FV to generate Similarity Heat Maps (S-HMs) and Dissimilarity Heat Maps (D-HMs) which offer reliable explanations to all types of FV decisions. Qualitative and quantitative experimental results show the effectiveness of the proposed FV explainability method beyond state-of-the-art benchmarks.
- KonferenzbeitragFace verification explainability heatmap generation using a vision transformer(BIOSIG 2023, 2023) Ricardo Correia, Paulo L CorreiaExplainable Face Recognition (XFR) is a critical technology to support the large deployment of learning-based face recognition solutions. This paper aims at contributing to the more transparent usage of Vision Transformers (ViTs) for face verification (FV) tasks, by proposing a novel approach for generating FV explainability heatmaps, for both positive and negative decisions. The proposed solution leverages on the attention maps generated by a ViT and employs masking techniques to create masks based on the highlighted regions in the attention maps. These masks are applied to the pair of faces, and the masking technique with most impact on the decision is selected to be used to generate heatmaps for the probe-gallery pair of faces. These heatmaps offer valuable insights into the decision-making process, shedding light on the most important face regions for the verification outcome. The key novelty of this paper lies in the proposed approach for generating explainability heatmaps tailored for verification pairs in the context of ViT models, which combines the ViT attention maps regions of the probe-gallery pair to create masks that allow evaluating those region´s impact on the verification decision for both positive and negative decisions.
- KonferenzbeitragFacial image reconstruction and its influence to face recognition(BIOSIG 2023, 2023) Filip Pleško, Tomas GoldmannThis paper focuses on reconstructing damaged facial images using GAN neural networks. In addition, the effect of generating the missing part of the face on face recognition is investigated. The main objective of this work is to observe whether it is possible to increase the accuracy of face recognition by generating missing parts while maintaining a low false accept rate (FAR). A new model for generating the missing parts of a face has been proposed. For face-based recognition, state-of-the-art solutions from the DeepFace library and the QMagFace solution have been used.
- KonferenzbeitragUtility prediction performance of finger image quality assessment software(BIOSIG 2023, 2023) Olaf HennigerA biometric sample is the more utile for biometric recognition the greater the distance between the sample-specific non-mated and mated comparison score distributions. Finger image quality scores turn out to be only weakly correlated with the observed utility. This is worth investigating because finger image quality assessment software is widely used to predict the biometric utility of finger images in many public-sector applications. This paper shows that a weak correlation between predicted and observed utility does not matter if the quality scores are used to decide whether to discard or retain biometric samples for further processing. The important point is that useful samples are not mistakenly discarded or less useful samples are not mistakenly retained. This can be measured by quality-assessment false positive and false negative rates. In cost-benefit analyses, these metrics can be used to chose suitable quality-score thresholds for the use cases at hand.
- KonferenzbeitragFairness and Privacy in Voice Biometrics: A Study of Gender Influences Using wav2vec 2.0(BIOSIG 2023, 2023) Oubaida Chouchane, Michele PanarielloThis study investigates the impact of gender information on utility, privacy, and fairness in voice biometric systems, guided by the General Data Protection Regulation (GDPR) mandates, which underscore the need for minimizing the processing and storage of private and sensitive data, and ensuring fairness in automated decision-making systems. We adopt an approach that involves the fine-tuning of the wav2vec 2.0 model for speaker verification tasks, evaluating potential gender-related privacy vulnerabilities in the process. An adversarial technique is implemented during the fine-tuning process to obscure gender information within the speaker embeddings, thus bolstering privacy. Results from VoxCeleb datasets indicate our adversarial model increases privacy against uninformed attacks (AUC of 46.80\%), yet slightly diminishes speaker verification performance (EER of 3.89\%) compared to the non-adversarial model (EER of 2.37\%). The model's efficacy reduces against informed attacks (AUC of 96.27\%). Preliminary analysis of system performance is conducted to identify potential gender bias, thus highlighting the need for continued research to understand and enhance fairness, and the delicate interplay between utility, privacy, and fairness in voice biometric systems.
- KonferenzbeitragContactless Palmprint Recognition for Children(BIOSIG 2023, 2023) Akash M Godbole, Steven A GroszEffective distribution of nutritional and healthcare aid for children, particularly infants and toddlers, in the world’s least developed and most impoverished countries, is a major problem due to lack of reliable identification documents. We present a mobile based contactless palmprint recognition system, Child Palm-ID, which meets the requirements of usability, cost, and accuracy for child recognition. On a contactless child palmprint database, Child-PalmDB1, with 1,020 unique palms (age range of 6 mos. to 48 mos.), Child Palm-ID achieves a TAR=94.8% at FAR=0.1%. Child Palm-ID is also able to recognize adults, achieving a TAR=99.5% on the CASIA contactless palmprint database and a TAR=100% on the COEP contactless adult palmprint database, both at FAR=0.1%. For child palmprint images captured at an interval of five months with differences in standoff distance, illumination and motion blur, the TAR drops to 80.5% at FAR=0.1%. This indicates that more research remains in contactless child palmprint recognition.
- KonferenzbeitragCyclist Recognition from a Silhouette Set(BIOSIG 2023, 2023) Eijiro Makishima, Fumito ShinmuraPerson recognition from surveillance cameras can be useful for criminal investigations. Currently, gait recognition technology can identify walking individuals, but recognition of people riding bicycles has not been actively investigated, despite cycling being a popular mode of transportation. In this paper, we propose a method to recognize individuals riding bicycles (cyclists) using a silhouette set. We captured two types of cyclist data, normal and rush modes, from five different views, and generated silhouette image sequences from this data. We evaluated accuracy of the proposed method on the silhouette images in identification and verification tasks. The evaluation results demonstrate the effectiveness of our proposed method.