Longitudinal Study of Child Face Recognition

Deb, Debayan; Nain, Neeta; Jain, Anil K.

doi:10.1109/icb2018.2018.00042

Cited by 53 publications

(36 citation statements)

References 25 publications

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“…In recent years, several works have been published that demonstrated the influence of demographics on commercial and open-sources face recognition algorithms. Studies [56], [46], [16], [66] analysing the impact of age demonstrated a lower biometric performance on faces of children. Studies [76], [3], [2], [61] analysing the effect of gender on face recognition showed that the recognition performance of females is weaker than the performance on male faces.…”

Section: A Estimating Bias In Face Recognitionmentioning

confidence: 99%

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A Comprehensive Study on Face Recognition Biases Beyond Demographics

Terhörst

Kolf

Huber

et al. 2022

IEEE Trans. Technol. Soc.

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Face recognition (FR) systems have a growing effect on critical decision-making processes. Recent works have shown that FR solutions show strong performance differences based on the user's demographics. However, to enable a trustworthy FR technology, it is essential to know the influence of an extended range of facial attributes on FR beyond demographics. Therefore, in this work, we analyse FR bias over a wide range of attributes. We investigate the influence of 47 attributes on the verification performance of two popular FR models. The experiments were performed on the publicly available MAAD-Face attribute database with over 120M high-quality attribute annotations. To prevent misleading statements about biased performances, we introduced control group based validity values to decide if unbalanced test data causes the performance differences. The results demonstrate that also many non-demographic attributes strongly affect the recognition performance, such as accessories, hair-styles and colors, face shapes, or facial anomalies. The observations of this work show the strong need for further advances in making FR system more robust, explainable, and fair. Moreover, our findings might help to a better understanding of how FR networks work, to enhance the robustness of these networks, and to develop more generalized bias-mitigating face recognition solutions.

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Section: A Estimating Bias In Face Recognitionmentioning

confidence: 99%

“…Identities Images Attributes (number classes) Ricanek et al [56] 0.7k 8.0k Age (2) Deb et al [16] 0.9k 3.7k Age (cont.) Srinivas et al [66] 1.7k 9.2k Age (2) Michalski et al [46] -4.7M Age (cont.)…”

Section: Workmentioning

confidence: 99%

A Comprehensive Study on Face Recognition Biases Beyond Demographics

Terhörst

Kolf

Huber

et al. 2022

IEEE Trans. Technol. Soc.

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“…For there should be a number of images/samples each participating child/subject during a specified of age. To analyze the capability of face recognition technology to trace lost children, several databases have been created [6], [104], [105].…”

Section: B Missing Children Identificationmentioning

confidence: 99%

“…Children Longitudinal Face (CLF) dataset [105] consists of 3,682 face images of 919 participating children aged between two and 18 years. Each child had at least four face images acquired over a time span of up to six years.…”

Section: B Missing Children Identificationmentioning

confidence: 99%

A Comprehensive Literature Review on Children’s Databases for Machine Learning Applications

2022

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The COVID-19 pandemic can be attributed as a main factor to accelerate the current digital transformation and to encourage innovation and technological adoption. Consequently, the care provided to our children, one of the significant aspects of life, needs to be adapted with the life's changes. Children are our future and our most precious resources. They need our attention in all life domains including health, education, safety and social interaction. Nowadays, technologies have been incorporated with machine learning and it has been proven that they are more powerful, reliable and profitable. Machine learning methods have been applied by many children-related studies to generate predictive models for different applications. The efficacy of the generated models mainly rely on the constructed databases. This article carries out a comprehensive survey on available children's databases constructed for machine-learningbased solutions with their methodologies, characteristics, challenges, and applications. First, it provides an overview of the available studies and classifies them based on their applications. Next, it defines a set of attributes and evaluates them while also shedding light on their pros and cons. The primary concerns related to collection, development and distribution of children's databases are also discussed. This study can be considered as a guideline for researchers in multidisciplinary fields to construct reliable databases and to develop more advanced techniques.

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“…Several studies have analyzed the extent to which facial aging affects the performance of AFR. Two major conclusions can be drawn based on these studies: (i) Performance decreases with an increase in time lapse between subsequent image acquisitions [25,14,22], and (ii) performance degrades more rapidly in the case of younger individuals than older individuals [22,15]. Figure 1 illustrates that a state-of-the-art face matcher (CosFace) fails when it comes to matching an enrolled child in the gallery with the corresponding probe over large time lapses.…”

Section: Introductionmentioning

confidence: 98%

Child Face Age-Progression via Deep Feature Aging

Deb¹,

Aggarwal²,

Jain³

2020

Preprint

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Given a gallery of face images of missing children, state-of-the-art face recognition systems fall short in identifying a child (probe) recovered at a later age. We propose a feature aging module that can age-progress deep face features output by a face matcher. In addition, the feature aging module guides age-progression in the image space such that synthesized aged faces can be utilized to enhance longitudinal face recognition performance of any face matcher without requiring any explicit training. For time lapses larger than 10 years (the missing child is found after 10 or more years), the proposed age-progression module improves the closed-set identification accuracy of FaceNet from 16.53% to 21.44% and CosFace from 60.72% to 66.12% on a child celebrity dataset, namely ITWCC. The proposed method also outperforms state-of-the-art approaches [49,39] with a rank-1 identification rate of 95.91%, compared to 94.91%, on a public aging dataset, FG-NET, and 99.58%, compared to 99.50%, on CACD-VS. These results suggest that aging face features enhances the ability to identify young children who are possible victims of child trafficking or abduction.

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Longitudinal Study of Child Face Recognition

Cited by 53 publications

References 25 publications

A Comprehensive Study on Face Recognition Biases Beyond Demographics

A Comprehensive Study on Face Recognition Biases Beyond Demographics

A Comprehensive Literature Review on Children’s Databases for Machine Learning Applications

Child Face Age-Progression via Deep Feature Aging

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