Analyzing Covariate Influence on Gender and Race Prediction From Near-Infrared Ocular Images

Bobeldyk, Denton; Ross, Arun

doi:10.1109/access.2018.2886275

“…In [50], the authors use NIR ocular images to estimate gender and race. They apply typical iris texture descriptors used for recognition (Binarised Statistical Image Feature, BSIF, Local Binary Patterns, LBP, and Local Phase Quantization, LPQ) with SVM classifiers.…”

Section: Related Work On Age and Gender Classification Using Ocular I...mentioning

confidence: 99%

Facial masks and soft‐biometrics: Leveraging face recognition CNNs for age and gender prediction on mobile ocular images

Alonso‐Fernandez

¹

,

Hernandez‐Diaz

²

,

Perales

³

et al. 2021

View full text Add to dashboard Cite

We address the use of selfie ocular images captured with smartphones to estimate age and gender. Partial face occlusion has become an issue due to the mandatory use of face masks. Also, the use of mobile devices has exploded, with the pandemic further accelerating the migration to digital services. However, state-of-the-art solutions in related tasks such as identity or expression recognition employ large Convolutional Neural Networks, whose use in mobile devices is infeasible due to hardware limitations and size restrictions of downloadable applications. To counteract this, we adapt two existing lightweight CNNs proposed in the context of the ImageNet Challenge, and two additional architectures proposed for mobile face recognition. Since datasets for softbiometrics prediction using selfie images are limited, we counteract over-fitting by using networks pre-trained on ImageNet. Furthermore, some networks are further pretrained for face recognition, for which very large training databases are available. Since both tasks employ similar input data, we hypothesise that such strategy can be beneficial for soft-biometrics estimation. A comprehensive study of the effects of different pretraining over the employed architectures is carried out, showing that, in most cases, a better accuracy is obtained after the networks have been fine-tuned for face recognition. | INTRODUCTIONRecent research has explored the automatic extraction of information such as gender, age, ethnicity, etc. of an individual, known as soft-biometrics [1]. It can be deduced from biometric data like face photos, voice, gait, hand or body images, etc. One of the most natural ways is face analysis [2], but given the use of masks due to the COVID-19 pandemic, the face appears occluded even in cooperative settings, leaving the ocular region as the only visible part. In recent years, the ocular region has gained attention as a stand-alone modality for a variety of tasks, including person recognition [3], softbiometrics estimation [4], or liveness detection [5]. Accordingly, this work is concerned with the challenge of estimating soft-biometrics when only the ocular region is available. Additionally, we are interested in mobile environments [6]. The pandemic has accelerated the migration to the digital domain, converting mobiles in data hubs used for all type of This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

show abstract

“…In [55], the authors use NIR ocular images to estimate gender and race. They apply typical iris texture descriptors used for recognition (Binarized Statistical Image Feature, BSIF, Local Binary Patterns, LBP, and Local Phase Quantization, LPQ) with SVM classifiers.…”

Section: Related Work On Age and Gender Classification Using Ocular I...mentioning

confidence: 99%

Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images

Alonso‐Fernandez¹,

Diaz²,

Perales³

et al. 2021

Preprint

View full text Add to dashboard Cite

We address the use of selfie ocular images captured with smartphones to estimate age and gender. Partial face occlusion has become an issue due to the mandatory use of face masks. Also, the use of mobile devices has exploded, with the pandemic further accelerating the migration to digital services. However, state-of-the-art solutions in related tasks such as identity or expression recognition employ large Convolutional Neural Networks, whose use in mobile devices is infeasible due to hardware limitations and size restrictions of downloadable applications. To counteract this, we adapt two existing lightweight CNNs proposed in the context of the ImageNet Challenge, and two additional architectures proposed for mobile face recognition. Since datasets for soft-biometrics prediction using selfie images are limited, we counteract over-fitting by using networks pre-trained on ImageNet. Furthermore, some networks are further pre-trained for face recognition, for which very large training databases are available. Since both tasks employ similar input data, we hypothesize that such strategy can be beneficial for soft-biometrics estimation. A comprehensive study of the effects of different pre-training over the employed architectures is carried out, showing that, in most cases, a better accuracy is obtained after the networks have been fine-tuned for face recognition.

show abstract

“…Bobeldyk and Ross [21] demonstrated that it is possible to use simple texture descriptors, such as binarized statistical image features and local binary patterns, to extract gender and race attributes from a near-infrared ocular image that is used in a typical iris recognition system. The proposed method can predict gender and race from a single iris image with an accuracy of 86% and 90%, respectively.…”

Section: Related Workmentioning

confidence: 99%

Classification of Genetically Identical Left and Right Irises Using a Convolutional Neural Network

Fang

¹

,

Lu

²

,

Zhou

³

et al. 2019

View full text Add to dashboard Cite

As one of the most reliable biometric identification techniques, iris recognition has focused on the differences in iris textures without considering the similarities. In this work, we investigate the correlation between the left and right irises of an individual using a VGG16 convolutional neural network. Experimental results with two independent iris datasets show that a remarkably high classification accuracy of larger than 94% can be achieved when identifying if two irises (left and right) are from the same or different individuals. This exciting finding suggests that the similarities between genetically identical irises that are indistinguishable using traditional Daugman’s approaches can be detected by deep learning. We expect this work will shed light on further studies on the correlation between irises and/or other biometric identifiers of genetically identical or related individuals, which would find potential applications in criminal investigations.

show abstract

Analyzing Covariate Influence on Gender and Race Prediction From Near-Infrared Ocular Images

Cited by 8 publications

References 45 publications

Facial masks and soft‐biometrics: Leveraging face recognition CNNs for age and gender prediction on mobile ocular images

Facial masks and soft‐biometrics: Leveraging face recognition CNNs for age and gender prediction on mobile ocular images

Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images

Classification of Genetically Identical Left and Right Irises Using a Convolutional Neural Network

Contact Info

Product

Resources

About