Finger Pose Estimation for Under-screen Fingerprint Sensor
Xiongjun Guan, Zhiyu Pan, Jianjiang Feng, Jie Zhou

TL;DR
This paper introduces a dual-modal neural network for under-screen fingerprint pose estimation, combining texture and contour data to improve accuracy and robustness in challenging conditions.
Contribution
It proposes a novel dual-modal input network with a decoupled probability prediction, MoE feature fusion, and cross-domain knowledge transfer for enhanced fingerprint pose estimation.
Findings
Outperforms previous SOTA methods on public and private datasets.
Significantly improves fingerprint recognition accuracy.
Demonstrates robustness to large angles and small areas.
Abstract
Two-dimensional pose estimation plays a crucial role in fingerprint recognition by facilitating global alignment and reduce pose-induced variations. However, existing methods are still unsatisfactory when handling with large angle or small area inputs. These limitations are particularly pronounced on fingerprints captured by under-screen fingerprint sensors in smartphones. In this paper, we present a novel dual-modal input based network for under-screen fingerprint pose estimation. Our approach effectively integrates two distinct yet complementary modalities: texture details extracted from ridge patches through the under-screen fingerprint sensor, and rough contours derived from capacitive images obtained via the touch screen. This collaborative integration endows our network with more comprehensive and discriminative information, substantially improving the accuracy and stability of…
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Taxonomy
TopicsBiometric Identification and Security · Face recognition and analysis
MethodsHeatmap
