2 research outputs found
Towards Palmprint Verification On Smartphones
With the rapid development of mobile devices, smartphones have gradually
become an indispensable part of people's lives. Meanwhile, biometric
authentication has been corroborated to be an effective method for establishing
a person's identity with high confidence. Hence, recently, biometric
technologies for smartphones have also become increasingly sophisticated and
popular. But it is noteworthy that the application potential of palmprints for
smartphones is seriously underestimated. Studies in the past two decades have
shown that palmprints have outstanding merits in uniqueness and permanence, and
have high user acceptance. However, currently, studies specializing in
palmprint verification for smartphones are still quite sporadic, especially
when compared to face- or fingerprint-oriented ones. In this paper, aiming to
fill the aforementioned research gap, we conducted a thorough study of
palmprint verification on smartphones and our contributions are twofold. First,
to facilitate the study of palmprint verification on smartphones, we
established an annotated palmprint dataset named MPD, which was collected by
multi-brand smartphones in two separate sessions with various backgrounds and
illumination conditions. As the largest dataset in this field, MPD contains
16,000 palm images collected from 200 subjects. Second, we built a DCNN-based
palmprint verification system named DeepMPV+ for smartphones. In DeepMPV+, two
key steps, ROI extraction and ROI matching, are both formulated as learning
problems and then solved naturally by modern DCNN models. The efficiency and
efficacy of DeepMPV+ have been corroborated by extensive experiments. To make
our results fully reproducible, the labeled dataset and the relevant source
codes have been made publicly available at
https://cslinzhang.github.io/MobilePalmPrint/
Towards Unconstrained Palmprint Recognition on Consumer Devices: a Literature Review
As a biometric palmprints have been largely under-utilized, but they offer
some advantages over fingerprints and facial biometrics. Recent improvements in
imaging capabilities on handheld and wearable consumer devices have re-awakened
interest in the use fo palmprints. The aim of this paper is to provide a
comprehensive review of state-of-the-art methods for palmprint recognition
including Region of Interest extraction methods, feature extraction approaches
and matching algorithms along with overview of available palmprint datasets in
order to understand the latest trends and research dynamics in the palmprint
recognition field