372 research outputs found
Three Dimensional Palmprint Recognition using Structured Light Imaging
BTAS 2008 - IEEE 2nd International Conference on Biometrics: Theory, Applications and Systems, Arlington, VA, 29-1 October 2008Palmprint is one of the most unique and stable biometric characteristics. Although 2D palmprint recognition can achieve high accuracy, the 2D palmprint images can be easily counterfeited and much 3D depth information is lost in the imaging process. This paper presents a new approach, 3D palmprint recognition, to exploit the 3D structural information of the palm surface. The structured-light imaging is used to acquire the 3D palmprint data, from which the features of Mean Curvature, Gauss Curvature and Surface Type (ST) are extracted. A fast feature matching and score level fusion strategy are then used to classify the input 3D palmprint data. With the established 3D palmprint database, a series of verification and identification experiments are conducted and the results show that 3D palmprint technique can achieve high recognition rate while having high anti-counterfeiting capability.Department of ComputingRefereed conference pape
RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition
Palmprint recently shows great potential in recognition applications as it is
a privacy-friendly and stable biometric. However, the lack of large-scale
public palmprint datasets limits further research and development of palmprint
recognition. In this paper, we propose a novel realistic pseudo-palmprint
generation (RPG) model to synthesize palmprints with massive identities. We
first introduce a conditional modulation generator to improve the intra-class
diversity. Then an identity-aware loss is proposed to ensure identity
consistency against unpaired training. We further improve the B\'ezier palm
creases generation strategy to guarantee identity independence. Extensive
experimental results demonstrate that synthetic pretraining significantly
boosts the recognition model performance. For example, our model improves the
state-of-the-art B\'ezierPalm by more than and in terms of
TAR@FAR=1e-6 under the and Open-set protocol. When accessing only
of the real training data, our method still outperforms ArcFace with
real training data, indicating that we are closer to real-data-free
palmprint recognition.Comment: 12 pages,8 figure
On the Feasibility of Interoperable Schemes in Hand Biometrics
Personal recognition through hand-based biometrics has attracted the interest of many researchers in the last twenty years. A significant number of proposals based on different procedures and acquisition devices have been published in the literature. However, comparisons between devices and their interoperability have not been thoroughly studied. This paper tries to fill this gap by proposing procedures to improve the interoperability among different hand biometric schemes. The experiments were conducted on a database made up of 8,320 hand images acquired from six different hand biometric schemes, including a flat scanner, webcams at different wavelengths, high quality cameras, and contactless devices. Acquisitions on both sides of the hand were included. Our experiment includes four feature extraction methods which determine the best performance among the different scenarios for two of the most popular hand biometrics: hand shape and palm print. We propose smoothing techniques at the image and feature levels to reduce interdevice variability. Results suggest that comparative hand shape offers better performance in terms of interoperability than palm prints, but palm prints can be more effective when using similar sensors
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