ICDAR 2021 Competition on On-Line Signature Verification - Tolosana, Ruben; Vera-Rodriguez, Ruben; Gonzalez-Garcia, Carlos; Fierrez, Julian; Rengifo, Santiago; Morales, Aythami; Ortega-Garcia, Javier; Carlos Ruiz-Garcia, Juan; Romero-Tapiador, Sergio; Jiang, Jiajia; Lai, Songxuan; Jin, Lianwen; et al. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021)
This paper describes the experimental framework and results of the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021). The goal of SVC 2021 is to evaluate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously considered on each task. The results obtained in SVC 2021 prove the high potential of deep learning methods. In particular, the best on-line signature verification system of SVC 2021 obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3). SVC 2021 will be established as an on-going competition (https://sites.google.com/view/SVC2021 ), where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases such as DeepSignDB (https://github.com/BiDAlab/DeepSignDB ) and SVC2021_EvalDB (https://github.com/BiDAlab/SVC2021_EvalDB ), and standard experimental protocols.
CRIS-Team Scopus Importer:2021-10-01
Keywords: Benchmark; Biometrics; Deep learning; DeepSignDB; Handwriting; On-line signature; SVC 2021; SVC2021_EvalDB

Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification - Schwinn, Leo; Bungert, Leon; Nguyen, An; Raab, René; Pulsmeyer, Falk; Precup, Doina; Eskofier, Björn; Zanca, Dario (162, 2022)
The reliability of neural networks is essential for their use in safety-critical applications. Existing approaches generally aim at improving the robustness of neural networks to either real-world distribution shifts (e.g., common corruptions and perturbations, spatial transformations, and natural adversarial examples) or worst-case distribution shifts (e.g., optimized adversarial examples). In this work, we propose the Decision Region Quantification (DRQ) algorithm to improve the robustness of any differentiable pre-trained model against both real-world and worst-case distribution shifts in the data. DRQ analyzes the robustness of local decision regions in the vicinity of a given data point to make more reliable predictions. We theoretically motivate the DRQ algorithm by showing that it effectively smooths spurious local extrema in the decision surface. Furthermore, we propose an implementation using targeted and untargeted adversarial attacks. An extensive empirical evaluation shows that DRQ increases the robustness of adversarially and non-adversarially trained models against real-world and worst-case distribution shifts on several computer vision benchmark datasets.<br
Keywords: Deep Learning; Robustness; Adversarial examples

SVC-onGoing: Signature verification competition - Tolosana, Ruben; Vera-Rodriguez, Ruben; Gonzalez-Garcia, Carlos; Fierrez, Julian; Morales, Aythami; Ortega-Garcia, Javier; Carlos Ruiz-Garcia, Juan; Romero-Tapiador, Sergio; Rengifo, Santiago; Caruana, Miguel; Jiang, Jiajia; Lai, Songxuan; et al. (Pattern Recognition, 2022)
This article presents SVC-onGoing1, an on-going competition for on-line signature verification where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases, such as DeepSignDB2 and SVC2021_EvalDB3, and standard experimental protocols. SVC-onGoing is based on the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021), which has been extended to allow participants anytime. The goal of SVC-onGoing is to evaluate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously considered on each task. The results obtained in SVC-onGoing prove the high potential of deep learning methods in comparison with traditional methods. In particular, the best signature verification system has obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3). Future studies in the field should be oriented to improve the performance of signature verification systems on the challenging mobile scenarios of SVC-onGoing in which several mobile devices and the finger are used during the signature acquisition.
Created from Fastlane, Scopus look-up
Keywords: Biometrics; DeepSignDB; Handwriting; Signature verification; SVC 2021; SVC-onGoing; SVC2021_EvalDB

xLength: Predicting Expected Ski Jump Length Shortly after Take-Off Using Deep Learning - Link, Johannes; Schwinn, Leo; Pulsmeyer, Falk; Kautz, Thomas; Eskofier, Björn (Sensors, 2022)
With tracking systems becoming more widespread in sports research and regular training and competitions, more data are available for sports analytics and performance prediction. We analyzed 2523 ski jumps from 205 athletes on five venues. For every jump, the dataset includes the 3D trajectory, 3D velocity, skis’ orientation, and metadata such as wind, starting gate, and ski jumping hill data. Using this dataset, we aimed to predict the expected jump length (xLength) inspired by the expected goals metric in soccer (xG). We evaluate the performance of a fully connected neural network, a convolutional neural network (CNN), a long short-term memory (LSTM), and a ResNet architecture to estimate the xLength. For the prediction of the jump length one second after take-off, we achieve a mean absolute error (MAE) of 5.3 (Formula presented.) for the generalization to new athletes and an MAE of 5.9 (Formula presented.) for the generalization to new ski jumping hills using ResNet architectures. Additionally, we investigated the influence of the input time after the take-off on the predictions’ accuracy. As expected, the MAE becomes smaller with longer inputs. Due to the real-time transmission of the sensor’s data, xLength can be updated during the flight phase and used in live TV broadcasting. xLength could also be used as an analysis tool for experts to quantify the quality of the take-off and flight phases.
CRIS-Team Scopus Importer:2022-11-25
Keywords: inertial measurement unit; performance analysis; performance prediction; sports analytics; ultra-wideband; wearable sensors