Introduction to Selfie Biometrics

Rattani, Ajita; Derakhshani, Reza; Ross, Arun

doi:10.1007/978-3-030-26972-2_1

Cited by 10 publications

(14 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The lens discussion is important here, since proposed selfie lenses feature a wide aperture of f/1.4which, combined with a longer focal length, mimics a portrait lens and allows for the articulation of a sharp face against a blurry background. 48 Here, selfie biometrics is defined as "an authentication mechanism where a user captures images of her biometric traits (such as the face or ocular region) by using the imaging sensors available in the device itself." 49 The idea of the selfie here has again shifted away from modes of representation and agency, towards an automated "capture" of biometric traits.…”

Section: Selfie To Self-capturementioning

confidence: 99%

Portraiture, Surveillance, and the Continuity Aesthetic of Blur

Hristova¹

2021

Frames

View full text Add to dashboard Cite

Section: Selfie To Self-capturementioning

confidence: 99%

Portraiture, Surveillance, and the Continuity Aesthetic of Blur

Hristova¹

2021

Frames

View full text Add to dashboard Cite

“…Face recognition is one of the most used biological features for user authentication. Generally, face recognition authentication is conducted by taking an image with the smartphone camera and extracting its local features, which are then utilized as inputs of a classifier [ 53 ].…”

Section: Continuous Authenticationmentioning

confidence: 99%

Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review

Hernández–Álvarez

Fuentes

González-Manzano

et al. 2020

Sensors

View full text Add to dashboard Cite

Ensuring the confidentiality of private data stored in our technological devices is a fundamental aspect for protecting our personal and professional information. Authentication procedures are among the main methods used to achieve this protection and, typically, are implemented only when accessing the device. Nevertheless, in many occasions it is necessary to carry out user authentication in a continuous manner to guarantee an allowed use of the device while protecting authentication data. In this work, we first review the state of the art of Continuous Authentication (CA), User Profiling (UP), and related biometric databases. Secondly, we summarize the privacy-preserving methods employed to protect the security of sensor-based data used to conduct user authentication, and some practical examples of their utilization. The analysis of the literature of these topics reveals the importance of sensor-based data to protect personal and professional information, as well as the need for exploring a combination of more biometric features with privacy-preserving approaches.

show abstract

“…Figure 1. Sample standard face images However, in the recent years gender recognition became more challenging, especially when dealing with unconstrained real-world photos [13] which depicts full/partial occluded frontal facial images that depicts extreme viewing angles. This situation is best illustrated in the contemporary selfie photos, which are hard to analyze using the systematic facial-based approaches [14].…”

Section: Introductionmentioning

confidence: 99%

Gender recognition from unconstrained selfie images: a convolutional neural network approach

Bekhet

Alghamdi

Taj-Eddin

2022

IJECE

View full text Add to dashboard Cite

<p>Human gender recognition is an essential demographic tool. This is reflected in forensic science, surveillance systems and targeted marketing applications. This research was always driven using standard face images and hand-crafted features. Such way has achieved good results, however, the reliability of the facial images had a great effect on the robustness of extracted features, where any small change in the query facial image could change the results. Nevertheless, the performance of current techniques in unconstrained environments is still inefficient, especially when contrasted against recent breakthroughs in different computer vision research. This paper introduces a novel technique for human gender recognition from non-standard selfie images using deep learning approaches. Selfie photos are uncontrolled partial or full-frontal body images that are usually taken by people themselves in real-life environment. As far as we know this is the first paper of its kind to identify gender from selfie photos, using deep learning approach. The experimental results on the selfie dataset emphasizes the proposed technique effectiveness in recognizing gender from such images with 89% accuracy. The performance is further consolidated by testing on numerous benchmark datasets that are widely used in the field, namely: Adience, LFW, FERET, NIVE, Caltech WebFaces and<br />CAS-PEAL-R1.</p>

show abstract

Introduction to Selfie Biometrics

Cited by 10 publications

References 55 publications

Portraiture, Surveillance, and the Continuity Aesthetic of Blur

Portraiture, Surveillance, and the Continuity Aesthetic of Blur

Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review

Gender recognition from unconstrained selfie images: a convolutional neural network approach

Contact Info

Product

Resources

About