Towards Palmprint Verification On Smartphones

• URL: http://arxiv.org/abs/2003.13266v2
• Date: Mon, 3 Aug 2020 04:08:04 GMT
• Title: Towards Palmprint Verification On Smartphones
• Authors: Yingyi Zhang, Lin Zhang, Ruixin Zhang, Shaoxin Li, Jilin Li, Feiyue Huang
• Abstract summary: Studies in the past two decades have shown that palmprints have outstanding merits in uniqueness and permanence. We built a DCNN-based palmprint verification system named DeepMPV+ for smartphones. The efficiency and efficacy of DeepMPV+ have been corroborated by extensive experiments.
• Score: 62.279124220123286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/.

Related papers

• EgoPressure: A Dataset for Hand Pressure and Pose Estimation in Egocentric Vision [69.1005706608681]
  We introduce EgoPressure, a novel dataset of touch contact and pressure interaction from an egocentric perspective. EgoPressure comprises 5.0 hours of touch contact and pressure interaction from 21 participants captured by a moving egocentric camera and 7 stationary Kinect cameras.
  arXiv  Detail & Related papers  (2024-09-03T18:53:32Z)
• Mobile Contactless Palmprint Recognition: Use of Multiscale, Multimodel Embeddings [26.774583290826193]
  This study integrates a vision transformer (ViT) and a convolutional neural network (CNN) to extract complementary local and global features. Next, a mobile-based, end-to-end palmprint recognition system is developed, referred to as Palm-ID. Palm-ID balances the trade-off between accuracy and latency, requiring just 18ms to extract a template of size 516 bytes.
  arXiv  Detail & Related papers  (2024-01-16T04:42:54Z)
• RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition [26.82617300073746]
  We propose a novel realistic pseudo-palmprint generation model to synthesize palmprints with massive identities. Our model improves the state-of-the-art B'ezierPalm by more than $5%$ and $14%$ in terms of TAR@FAR=1e-6 under the $1:1$ and $1:3$ Open-set protocol. Our method still outperforms ArcFace with $100%$ real training data, indicating that we are closer to real-data-free palmprint recognition.
  arXiv  Detail & Related papers  (2023-07-26T07:57:56Z)
• Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report [59.831324427712815]
  This challenge aims to develop an efficient end-to-end AI-based image signal processing pipeline. The models were evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results.
  arXiv  Detail & Related papers  (2022-11-07T22:13:10Z)
• Mobile Behavioral Biometrics for Passive Authentication [65.94403066225384]
  This work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits. Experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke.
  arXiv  Detail & Related papers  (2022-03-14T17:05:59Z)
• Touchless Palmprint Recognition based on 3D Gabor Template and Block Feature Refinement [28.991303988737503]
  We build a large-scale touchless palmprint dataset containing 2334 palms from 1167 individuals. To our best knowledge, it is the largest contactless palmprint image benchmark ever collected. We propose a novel deep learning framework for touchless palmprint recognition named 3DCPN.
  arXiv  Detail & Related papers  (2021-03-03T04:22:24Z)
• TapNet: The Design, Training, Implementation, and Applications of a Multi-Task Learning CNN for Off-Screen Mobile Input [75.05709030478073]
  We present the design, training, implementation and applications of TapNet, a multi-task network that detects tapping on the smartphone. TapNet can jointly learn from data across devices and simultaneously recognize multiple tap properties, including tap direction and tap location.
  arXiv  Detail & Related papers  (2021-02-18T00:45:41Z)
• Families In Wild Multimedia: A Multimodal Database for Recognizing Kinship [63.27052967981546]
  We introduce the first publicly available multi-task MM kinship dataset. To build FIW MM, we developed machinery to automatically collect, annotate, and prepare the data. Results highlight edge cases to inspire future research with different areas of improvement.
  arXiv  Detail & Related papers  (2020-07-28T22:36:57Z)
• Towards Efficient Unconstrained Palmprint Recognition via Deep Distillation Hashing [28.36096948563473]
  Deep Distillation Hashing (DDH) is proposed as benchmark for efficient deep palmprint recognition. The accuracy of palmprint identification can be increased by up to 11.37%, and the Equal Error Rate (EER) of palmprint verification can be reduced by up to 3.11%.
  arXiv  Detail & Related papers  (2020-04-07T12:15:04Z)
• Towards Unconstrained Palmprint Recognition on Consumer Devices: a Literature Review [0.34376560669160383]
  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.
  arXiv  Detail & Related papers  (2020-03-02T09:53:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.