Deep Gender Classification and Visualization of Near-Infra-Red Periocular-Iris images

Viedma, Ignacio; Tapia, Juan

doi:10.1109/ipas.2018.8708857

Cited by 3 publications

(15 citation statements)

References 23 publications

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“…Datasets for age and gender prediction from social media are still relatively limited [1]. To counteract over-fitting, some works use small CNNs of 2 or 3 convolutional layers trained from scratch [16,17,35,37]. To be able to use more complex networks, one possibility is to pre-train them on a generic task for which large databases exist, like ImageNet [34].…”

Section: Contributionsmentioning

confidence: 99%

“…To be able to use more complex networks, one possibility is to pre-train them on a generic task for which large databases exist, like ImageNet [34]. This is done for example in [19] [35], and in the present paper. In the previous study, we employed CNNs pre-trained on ImageNet as well, and classification was done with Fig.…”

Section: Contributionsmentioning

confidence: 99%

“…In subsequent works, Viedma et al [35,50] study gender classification with NIR ocular images. In [35], they train two small CNNs of 2 and 3 convolutional layers from scratch.…”

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

confidence: 99%

“…In subsequent works, Viedma et al [35,50] study gender classification with NIR ocular images. In [35], they train two small CNNs of 2 and 3 convolutional layers from scratch. They also use the very deep VGG-16, VGG-19 and Resnet-50 architectures (pretrained on ImageNet).…”

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

confidence: 99%

“…Examples include the generic ImageNet Challenge [34], as done e.g. in [19,35], or face recognition datasets [32,36]. Both are approaches that we follow in the present paper as well.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Contributionsmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

“…In subsequent works, Viedma et al [35,50] study gender classification with NIR ocular images. In [35], they train two small CNNs of 2 and 3 convolutional layers from scratch.…”

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

confidence: 99%

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

confidence: 99%

“…Examples include the generic ImageNet Challenge [34], as done e.g. in [19,35], or face recognition datasets [32,36]. Both are approaches that we follow in the present paper as well.…”

Section: Introductionmentioning

confidence: 99%

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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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Impact of Occlusion Masks on Gender Classification from Iris Texture

Yáñez,

Tapia,

Perez

et al. 2024

IET Biometrics

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Gender classification on normalized iris images has been previously attempted with varying degrees of success. In these previous studies, it has been shown that occlusion masks may introduce gender information; occlusion masks are used in iris recognition to remove non-iris elements. When, the goal is to classify the gender using exclusively the iris texture, the presence of gender information in the masks may result in apparently higher accuracy, thereby not reflecting the actual gender information present in the iris. However, no measures have been taken to eliminate this information while preserving as much iris information as possible. We propose a novel method to assess the gender information present in the iris more accurately by eliminating gender information in the masks. This consists of pairing iris with similar masks and different gender, generating a paired mask using the OR operator, and applying this mask to the iris. Additionally, we manually fix iris segmentation errors to study their impact on the gender classification. Our results show that occlusion masks can account for 6.92% of the gender classification accuracy on average. Therefore, works aiming to perform gender classification using the iris texture from normalized iris images should eliminate this correlation.

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

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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Deep Gender Classification and Visualization of Near-Infra-Red Periocular-Iris images

Cited by 3 publications

References 23 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

Impact of Occlusion Masks on Gender Classification from Iris Texture

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

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