Effect of face and ocular multimodal biometric systems on gender classification

Eskandari, Maryam; Sharifi, Omid

doi:10.1049/iet-bmt.2018.5134

“…The hypothesis that the iris included age and gender information was confirmed by experimental data. Moreover, Eskandari & Sharifi ( 2019), were tasked with analyzing face-ocular multimodal biometric systems in order to estimate a person's gender. They presented the first study to explore the fusion of facial and ocular biometrics in order to estimate a person's gender using a hybrid multimodal approach.…”

Section: Literatures Reviewmentioning

confidence: 99%

A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis

Hasan

¹

,

Mstafa

²

2022

SJUOZ

0

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Gender classification is attractive in a range of applications, including surveillance and monitoring, corporate profiling, and human-computer interaction. Individuals' identities may be gleaned from information about their gender, which is a kind of soft biometric. Over the years, several methods for determining a person's gender have been devised. Some of the most well-known ones are based on physical characteristics like face, fingerprint, palmprint, DNA, ears, gait, and iris. On the other hand, facial features account for the vast majority of gender classification methods. Also, the iris is a significant biometric trait, because the iris, according to research, remains basically constant during an individual's life. Besides that, the iris is externally visible and is non-invasive to the user, which is important for practical applications. Furthermore, there are already high-quality methods for segmenting and encoding iris images, and the current methods facilitate selecting and extracting attribute vectors from iris textures. This study discusses several approaches to determining gender. The previous works of literature are briefly reviewed. Additionally, there are a variety of methodologies for different steps of gender classification. This study provides researchers with knowledge and analysis of the existing gender classification approaches. Also, it will assist researchers who are interested in this specific area, as well as highlight the gaps and challenges in the field, and finally provide suggestions and future paths for improvement.

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“…Lastly, in [51], it is proposed a multimodal system that fuses features from the face and ocular regions. They use 300 NIR images of CASIA-Iris-Distance, and 405 VIS images of the MBGC database (one third are faces, one third are left eye, and one third are right eye images).…”

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

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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.

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“…Lastly, in [56], it is proposed a multimodal system that fuses features from the face and ocular regions.…”

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

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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.

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Effect of face and ocular multimodal biometric systems on gender classification

Cited by 16 publications

References 34 publications

A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis

A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis

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

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