P339 - BIOSIG 2023 - Proceedings of the 22nd International Conference of the Biometrics Special Interest Group
Auflistung nach:
Auflistung P339 - BIOSIG 2023 - Proceedings of the 22nd International Conference of the Biometrics Special Interest Group nach Schlagwort "Benchmarking"
1 - 7 von 7
Treffer pro Seite
Sortieroptionen
- KonferenzbeitragAutomatic validation of ICAO compliance regarding head coverings: an inclusive approach concerning religious circumstances(BIOSIG 2023, 2023) Carla Guerra, João S. MarcosThis paper contributes with a dataset and an algorithm that automatically verifies the compliance with the ICAO requirements related to the use of head coverings on facial images used on machine-readable travel documents. All the methods found in the literature ignore that some coverings might be accepted because of religious or cultural reasons, and basically only look for the presence of hats/caps. Our approach specifically includes the religious cases and distinguishes the head coverings that might be considered compliant. We built a dataset composed by facial images of 500 identities to accommodate these type of accessories. That data was used to fine-tune and train a classification model based on the YOLOv8 framework and we achieved state of the art results with an accuracy of 99.1% and EER of 5.7%.
- KonferenzbeitragBenchmarking fixed-length Fingerprint Representations across different Embedding Sizes and Sensor Types(BIOSIG 2023, 2023) Tim Rohwedder, Daile Osorio RoigTraditional minutiae-based fingerprint representations consist of a variable-length set of minutiae. This necessitates a more complex comparison causing the drawback of high computational cost in one-to-many comparison. Recently, deep neural networks have been proposed to extract fixed-length embeddings from fingerprints. In this paper, we explore to what extent fingerprint texture information contained in such embeddings can be reduced in terms of dimension, while preserving high biometric performance. This is of particular interest, since it would allow to reduce the number of operations incurred at comparisons. We also study the impact in terms of recognition performance of the fingerprint textural information for two sensor types, i.e. optical and capacitive. Furthermore, the impact of rotation and translation of fingerprint images on the extraction of fingerprint embeddings is analysed. Experimental results conducted on a publicly available database reveal an optimal embedding size of 512 feature elements for the texture-based embedding part of fixed-length fingerprint representations. In addition, differences in performance between sensor types can be perceived. The source code of all experiments presented in this paper is publicly available at https://github.com/tim-rohwedder/fixed-length-fingerprint-extractors, so our work can be fully reproduced.
- KonferenzbeitragImpact of Data Breadth and Depth on Performance of Siamese Neural Network Model: Experiments with Two Behavioral Biometric Datasets(BIOSIG 2023, 2023) Ahmed Wahab, Daqing HouDeep learning models, such as the Siamese Neural Networks (SNN), have shown great potential in capturing the intricate patterns in behavioral data. However, the impact of dataset breadth (i.e., the number of subjects) and depth (i.e., the amount of data per subject) on the performance of these models remain unexplored. To this end, we have conducted extensive experiments using two publicly available large datasets (Aalto and BrainRun), varying both the number of training subjects and the number of samples per subject. Our results show that dataset depth plays a crucial role in capturing more intricate variations specific to individual subjects, thereby positively influencing the performance of the SNN models. On the other hand, increasing the dataset breadth enables the model to effectively capture more inter-subject variability, which proved to be a more significant factor in improving the overall model performance. Specifically, once a certain threshold for the number of training subjects is surpassed, breadth starts to dominate performance and the impact of dataset depth diminishes and disappears. These findings shed light on the importance of dataset breadth and depth in training deep learning models for behavioral biometrics and provide valuable insights for designing more effective authentication systems.
- KonferenzbeitragImpact of Image Context for Single Deep Learning Face Morphing Attack Detection(BIOSIG 2023, 2023) Joana Pimenta, Iurii MedvedevThe increase in security concerns due to technological advancements has led to the popularity of biometric approaches that utilize physiological or behavioral characteristics for enhanced recognition. Face recognition systems (FRSs) have become prevalent, but they are still vulnerable to image manipulation techniques such as face morphing attacks. This study investigates the impact of the alignment settings of input images on deep learning face morphing detection performance. We analyze the interconnections between the face contour and image context and suggest optimal alignment conditions for face morphing detection.
- KonferenzbeitragLVT Face Database: A benchmark database for visible and hidden face biometrics(BIOSIG 2023, 2023) Nélida Mirabet-Herranz, Jean-Luc DugelayAlthough the estimation of eHealth parameters from face visuals (images and videos) has grown as a major area of research in the past years, deep-learning-based models are still challenged by RGB lack of robustness, for instance with changing illumination conditions. As a means to overcome these limitations and to unlock new opportunities, thermal imagery has arisen as a favorable alternative to solidify different technologies such as heart rate estimation from faces. However, the reduced number of databases containing thermal imagery and the lack of health annotation of the subjects in them limits the exploration of this spectrum. Motivated by this, in this paper, we present our Label-EURECOM Visible and Thermal (LVT) Face Database for face biometrics. This database is the first that contains paired visible and thermal images and videos from 52 subjects with metadata of 22 soft biometrics and health parameters. Moreover, we establish the first study introducing the potential of thermal images for weight estimation from faces on our database.
- 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.
- KonferenzbeitragA Wrist-worn Diffuse Optical Tomography Biometric System(BIOSIG 2023, 2023) Satya Sai Siva Rama Krishna Akula, Sumanth DasariWe present a diffuse optical tomography-based biometric system that is not dependent on external traits such as face, but rather interior anatomical information for better privacy and security. The Diffuse Optical Tomography (DOT) scanner is in the form of a wearable over the lower forearm and the wrist, where anatomical structures in the optical path of the scanner optodes serve as the basis for the unique biometric patterns. Our DOT scanner is low-cost and uses COTS Near Infrared LEDs and sensors. To supplement the DOT, our design also incorporates wrist vein imaging as a secondary modality. This paper details the design of the wristband, data collection, and machine learning-based analysis to show the utility of the DOT as a stand-alone biometric modality, and the efficacy of fusing DOT and wrist vein modalities. Our early experimental findings show promise, achieving a high area under the receiver operating characteristic curve (0.989).