Eyebrows and eyeglasses as soft biometrics using deep learning

Mohammad, Ahmad Saeed; Rattani, Ajita; Derakhshani, Reza

doi:10.1049/iet-bmt.2018.5230

Cited by 9 publications

(14 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mohammad et al. [10] investigated short‐term eyebrow recognition in the presence of eyeglasses using VISOB and FERET data sets. Regarding the short‐term verification using eyebrows, the authors proposed using a fusion of GIST, HOG, and VGG16 features.…”

Section: Prior Workmentioning

confidence: 99%

“…Previous work on eyebrows as a standalone biometric [10,11,[16][17][18] has been carried out under subject-dependent protocols. In order to make our evaluation more relevant to real-world applications, we evaluated various mobilefriendly deep learning models for eyebrow recognition using a subject-independent protocol.…”

Section: Introductionmentioning

confidence: 99%

“…Among ocular modalities, the lower periocular space (the region immediately below the lower eyelid) and the iris have received more attention due to their accuracy and added security [3,[6][7][8]. However, studies and everyday experience have revealed challenges related to iris and lower periocular recognition, including the almost universal lack of NIR iris scanners in mobile phones, droopy eyelids, cosmetic lenses, gazing, and eyeglasses reflections for iris modality [9,10]. Moreover, occlusions due to makeup, eyeglasses, and flu masks partially or completely obscuring the lower periocular region.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Subject independent evaluation of eyebrows as a stand‐alone biometric

2021

Self Cite

View full text Add to dashboard Cite

Considering challenges such as occlusions due to face coverings, ocular biometrics have risen as a potential remedy. Visible light ocular modalities use features extracted from in and around the eyes from 'selfie' captures for personal identification. A less-visited ocular neighbourhood is the eyebrows region. Previous works on eyebrows have been mostly using subject-specific verification protocols where identities overlap between the training and test sets and do not directly address the issue of doppelgangers and twins. Here, the evaluation of five deep learning models, lightCNN, ResNet, DenseNet, MobileNetV2, and SqueezeNet, for eyebrow-based user authentication in a subject independent environment across different data sets, lighting conditions, resolutions, and facial expressions is done. Also a challenging simulated identical twins scenario in our training and testing data sets is presented. Our results show a 4.2% equal error rate (EER) and 0.993 area under the curve (AUC) on a subset of the FACES data set (154 subjects) and 6.8% EER, 0.978 AUC on a subset of the VISOB data set (350 subjects). Despite achieving promising accuracy during short-term evaluations, the proposed methods showed lower accuracy when matching against long-term data or when facing data captured under varying illumination and facial expressions.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

Section: Prior Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Subject independent evaluation of eyebrows as a stand‐alone biometric

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…The deep learning method learns to extract features from input images. Some of the figure parts are adapted from [10,11] BEKHET AND ALAHMER -75 � Most of the training data for ML-based approaches were artificially stamped with eyeglasses [6], as the frame images were aligned for superposition using facial landmarks, that is, eyebrow, eye, ear, and nose [6]. Furthermore, the diversification of real glasses shape, makes the artificial stamping neither accurate nor representative of real training and testing images.…”

Section: F I G U R Ementioning

confidence: 99%

A robust deep learning approach for glasses detection in non‐standard facial images

Bekhet

Alahmer

2020

IET Biometrics

View full text Add to dashboard Cite

Automated glasses detection is a cardinal component in facial/ocular analysis that powers forensic, surveillance and biometric authentication systems. Throughout literature, glasses detection was always experimented by either utilizing hand‐crafted or deep learning features. Nevertheless, in both cases, highly standard face/ocular images were needed to derive the suggested technique. Both working methods performed reasonably well, but the results were bonded to the quality of the facial image and extracted features, where a slight shift and/or rotation in the input face image negatively affects the results. In addition, the obtained performance is even worse on real‐world (non‐standard) images, especially when compared to recent achievements in other computer vision research areas. In this paper, we present a robust deep learning approach for glasses detection from selfie photos full/partial frontal body non‐standard images captured in real‐life uncontrolled environments that do not utilize any facial landmarks. To the best of our knowledge this paper is the first to experiment detecting glasses from selfie photos, using a robust deep learning approach. Experimental results on various benchmark facial analysis datasets demonstrated the superior performance of the proposed technique with 96% accuracy.

show abstract

“…They improved performance by up to 15%. Furthermore, Mohammad et al (2019) solved one of the challenges, that is, face with eyeglasses, using an eyeglasses frame removal method to improve face recognition on datasets containing eyeglasses. An overview of the current face recognition system response to occluded areas and how they solved the occlusion challenges was published by Zeng et al (2021).…”

Section: Introductionmentioning

confidence: 99%

The contribution of different face parts to deep face recognition

Lestriandoko

Veldhuis

Spreeuwers

2022

Front. Comput. Sci.

View full text Add to dashboard Cite

The development of face recognition improvements still lacks knowledge on what parts of the face are important. In this article, the authors present face parts analysis to obtain important recognition information in a certain area of the face, more than just the eye or eyebrow, from the black box perspective. In addition, the authors propose a more advanced way to select parts without introducing artifacts using the average face and morphing. Furthermore, multiple face recognition systems are used to analyze the face component contribution. Finally, the results show that the four deep face recognition systems produce a different behavior for each experiment. However, the eyebrows are still the most important part of deep face recognition systems. In addition, the face texture played an important role deeper than the face shape.

show abstract

Eyebrows and eyeglasses as soft biometrics using deep learning

Cited by 9 publications

References 27 publications

Subject independent evaluation of eyebrows as a stand‐alone biometric

Subject independent evaluation of eyebrows as a stand‐alone biometric

A robust deep learning approach for glasses detection in non‐standard facial images

The contribution of different face parts to deep face recognition

Contact Info

Product

Resources

About