One-shot Representational Learning for Joint Biometric and Device Authentication

• URL: http://arxiv.org/abs/2101.00524v1
• Date: Sat, 2 Jan 2021 22:29:29 GMT
• Title: One-shot Representational Learning for Joint Biometric and Device Authentication
• Authors: Sudipta Banerjee and Arun Ross
• Abstract summary: We propose a method to simultaneously perform (i.e., identify the individual) and device recognition from a single biometric image. Such a joint recognition scheme can be useful in devices such as smartphones for enhancing security as well as privacy.
• Score: 14.646962064352577
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose a method to simultaneously perform (i) biometric recognition (i.e., identify the individual), and (ii) device recognition, (i.e., identify the device) from a single biometric image, say, a face image, using a one-shot schema. Such a joint recognition scheme can be useful in devices such as smartphones for enhancing security as well as privacy. We propose to automatically learn a joint representation that encapsulates both biometric-specific and sensor-specific features. We evaluate the proposed approach using iris, face and periocular images acquired using near-infrared iris sensors and smartphone cameras. Experiments conducted using 14,451 images from 15 sensors resulted in a rank-1 identification accuracy of upto 99.81% and a verification accuracy of upto 100% at a false match rate of 1%.

Related papers

• Multibiometrics Using a Single Face Image [0.11184789007828977]
  We propose a novel multibiometric method that combines five biometric traits, i.e., face, iris, periocular, nose, eyebrow, that can be extracted from a single face image. The proposed method does not sacrifice the convenience of biometrics since only a single face image is used as input.
  arXiv  Detail & Related papers  (2024-09-30T06:55:01Z)
• Unrecognizable Yet Identifiable: Image Distortion with Preserved Embeddings [22.338328674283062]
  We introduce an innovative image transformation technique that renders facial images unrecognizable to the eye while maintaining their identifiability by neural network models. The proposed methodology can be used in various artificial intelligence applications to distort the visual data and keep the derived features close. We show that it is possible to build the distortion that changes the image content by more than 70% while maintaining the same recognition accuracy.
  arXiv  Detail & Related papers  (2024-01-26T18:20:53Z)
• Whole-body Detection, Recognition and Identification at Altitude and Range [57.445372305202405]
  We propose an end-to-end system evaluated on diverse datasets. Our approach involves pre-training the detector on common image datasets and fine-tuning it on BRIAR's complex videos and images. We conduct thorough evaluations under various conditions, such as different ranges and angles in indoor, outdoor, and aerial scenarios.
  arXiv  Detail & Related papers  (2023-11-09T20:20:23Z)
• FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude [67.55994773068191]
  This paper presents the end-to-end design, development and evaluation of FarSight. FarSight is an innovative software system designed for whole-body (fusion of face, gait and body shape) biometric recognition. We test FarSight's effectiveness using the newly acquired IARPA Biometric Recognition and Identification at Altitude and Range dataset.
  arXiv  Detail & Related papers  (2023-06-29T16:14:27Z)
• Compact multi-scale periocular recognition using SAFE features [63.48764893706088]
  We present a new approach for periocular recognition based on the Symmetry Assessment by Feature Expansion (SAFE) descriptor. We use the sclera center as single key point for feature extraction, highlighting the object-like identity properties that concentrates to this point unique of the eye.
  arXiv  Detail & Related papers  (2022-10-18T11:46:38Z)
• Super-Resolution for Selfie Biometrics: Introduction and Application to Face and Iris [67.74999528342273]
  Lack of resolution has a negative impact on the performance of image-based biometrics. Super-resolution techniques have to be adapted for the particularities of images from a specific biometric modality. This chapter presents an overview of recent advances in super-resolution reconstruction of face and iris images.
  arXiv  Detail & Related papers  (2022-04-12T10:28:31Z)
• Gait-based Human Identification through Minimum Gait-phases and Sensors [0.45857634932098795]
  We present a gait identification technique based on temporal and descriptive statistic parameters of different gait phases as the features. It is possible to achieve high accuracy of over 95.5 percent by monitoring a single phase of the whole gait cycle through only a single sensor. It was also shown that the proposed methodology could be used to achieve 100 percent identification accuracy when the whole gait cycle was monitored through pelvis and foot sensors combined.
  arXiv  Detail & Related papers  (2021-10-15T02:09:45Z)
• DeepTeeth: A Teeth-photo Based Human Authentication System for Mobile and Hand-held Devices [0.0]
  teeth-photo is a new biometric modality for human authentication on mobile and hand held devices. Biometrics samples are acquired using the camera mounted on mobile device with the help of a mobile application having specific markers to register the teeth area.
  arXiv  Detail & Related papers  (2021-07-28T08:00:09Z)
• Harnessing Unrecognizable Faces for Face Recognition [87.80037162457427]
  We propose a measure of recognizability of a face image, implemented by a deep neural network trained using mostly recognizable identities. We show that accounting for recognizability reduces error rate of single-image face recognition by 58% at FAR=1e-5.
  arXiv  Detail & Related papers  (2021-06-08T05:25:03Z)
• Smartphone Camera De-identification while Preserving Biometric Utility [13.164846772893455]
  Photo Response Non Uniformity (PRNU) is often exploited to deduce the identity of the smartphone device whose camera or sensor was used to acquire a certain image. In this work, we design an algorithm that perturbs a face image acquired using a smartphone camera such that (a) sensor-specific details pertaining to the smartphone camera are suppressed (sensor anonymization); (b) the sensor pattern of a different device is incorporated (sensor spoofing); and (c) biometric matching using the perturbed image is not affected (biometric utility). Experiments conducted on the MICHE-I and OULU-NPU datasets
  arXiv  Detail & Related papers  (2020-09-17T19:48:43Z)
• Towards Face Encryption by Generating Adversarial Identity Masks [53.82211571716117]
  We propose a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks. TIP-IM provides 95%+ protection success rate against various state-of-the-art face recognition models.
  arXiv  Detail & Related papers  (2020-03-15T12:45:10Z)

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.