Age Prediction from Iris Biometrics

Erbilek, Meryem; Fairhurst, Michael; Abreu, Márjory Da Costa

doi:10.1049/ic.2013.0258

Cited by 28 publications

(12 citation statements)

References 17 publications

(23 reference statements)

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“…Just like physical shape, skin texture, and cognitive abilities, human eyes and visual behavior are fundamentally affected by the aging process [20,36]. For example, eye tracking studies found age-related differences in people's visual explorativeness, pupil reactions to certain visual stimuli, and error rates in eye movement tasks [36,42].…”

Section: Age and Gender Recognitionmentioning

confidence: 99%

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What Does Your Gaze Reveal About You? On the Privacy Implications of Eye Tracking

Kröger

Lutz

Müller

2020

IFIP Advances in Information and Communication Technology

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Technologies to measure gaze direction and pupil reactivity have become efficient, cheap, and compact and are finding increasing use in many fields, including gaming, marketing, driver safety, military, and healthcare. Besides offering numerous useful applications, the rapidly expanding technology raises serious privacy concerns. Through the lens of advanced data analytics, gaze patterns can reveal much more information than a user wishes and expects to give away. Drawing from a broad range of scientific disciplines, this paper provides a structured overview of personal data that can be inferred from recorded eye activities. Our analysis of the literature shows that eye tracking data may implicitly contain information about a user's biometric identity, gender, age, ethnicity, body weight, personality traits, drug consumption habits, emotional state, skills and abilities, fears, interests, and sexual preferences. Certain eye tracking measures may even reveal specific cognitive processes and can be used to diagnose various physical and mental health conditions. By portraying the richness and sensitivity of gaze data, this paper provides an important basis for consumer education, privacy impact assessments, and further research into the societal implications of eye tracking.

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Section: Age and Gender Recognitionmentioning

confidence: 99%

“…Dynamic facial expressions, such as smiles, may also be analyzed to infer the age of test subjects [17]. Other parameters utilized for computerized age-group recognition include iris size and iris texture [20].…”

Section: Age and Gender Recognitionmentioning

confidence: 99%

What Does Your Gaze Reveal About You? On the Privacy Implications of Eye Tracking

Kröger

Lutz

Müller

2020

IFIP Advances in Information and Communication Technology

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“…[16, 18, 19]. Previously, near‐infrared (NIR) iris images for age estimation were employed, taking advantage of available iris databases [61, 62]. These studies used geometric or textural information, attaining an accuracy of ∼64%.…”

Section: Related Work On Age and Gender Classification Using Ocular Imagesmentioning

confidence: 99%

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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“…Other biometric modalities have received rather less attention from the research community; however, investigations can be found for age prediction using modalities such as the handwritten signature [103,104]. The investigation of age prediction from gait data can be found in [44,105], while [46,106] investigate the prediction of age from iris data using different sets of geometric and texture features. In [45,100,107], the authors have investigated the prediction of age from voice data, and Merkel et al [108] examines the prediction of subject age from the fingerprint.…”

Section: Age Estimationmentioning

confidence: 99%

Predictive biometrics: a review and analysis of predicting personal characteristics from biometric data

Fairhurst

Abreu

2017

IET biom.

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Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, 'higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future.

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Age Prediction from Iris Biometrics

Cited by 28 publications

References 17 publications

What Does Your Gaze Reveal About You? On the Privacy Implications of Eye Tracking

What Does Your Gaze Reveal About You? On the Privacy Implications of Eye Tracking

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

Predictive biometrics: a review and analysis of predicting personal characteristics from biometric data

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