Child and Adult Classification Using Ratio of Head and Body Heights in Images

İnce, Ömer Faruk; Park, J. S.; Song, Jia; Yoon, BumChul

doi:10.7763/ijcce.2014.v3.304

Cited by 13 publications

(8 citation statements)

References 6 publications

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“…Age prediction from human body images was presented in [5] where authors used their training dataset which contained just 1500 elements. In the context of child detection, the method based on the ratio between head and body heights was also proposed in [6]. The greatest number of researches touched an area of gait-based human age classification ( [7], [8], [9]) but to our newest knowledge, there is any application for a single image age classification based on still body images.…”

Section: A Related Workmentioning

confidence: 99%

Face and silhouette based age estimation for child detection system

Lehmann¹,

Paziewski²,

Pacut³

2022

Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

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The problem of age estimation based on facial images is a well-known computer vision task that is widely applied in identification systems. With help of the special Dyzurnet.pl unit detecting Internet content, related to sexual abuse of children we slightly redefined a problem. Our Convolutional Neural Network (CNN) solution is focused on infants and prepubescents recognition and in the particular age ranges can be considered as the-state-of-the-art in children detection.Silhouette-based age estimation is often concentrated on the human gait or body proportions analysis. Single image age estimations on the dressed (fully or partly) body are not typically researched because of a lack of properly labeled data. In our work, we present the method used to train image preparation and the final effectiveness of age estimation of that kind.The proposed solution is a part of the system for responding to threats to children's safety in cyberspace with particular emphasis on child pornography

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Section: A Related Workmentioning

confidence: 99%

Face and silhouette based age estimation for child detection system

Lehmann¹,

Paziewski²,

Pacut³

2022

Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

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“…One of these is the head-to-body ratio, which is larger in children. Using the head-to-body ratio as a feature, Ince et al [17] performed age classification of pedestrians as child or adult. Additionally, children have shorter limbs, and their upper limbs appear closer to the ground.…”

Section: Biological Featuresmentioning

confidence: 99%

“…In 2001, Davis [14] classified pedestrians as adults or children based on their walking styles. Other early attempts on age estimation using vision-based gait include the works of [15], [16], [17], and [18] in 2005, 2010, 2014, and 2015 respectively. The first attempt on age estimation using sensor-based gait was made by Riaz et al [19] in 2015.…”

Section: Introductionmentioning

confidence: 99%

Learning Age From Gait: A Survey

et al. 2021

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Age is an important human attribute that needs to be determined for various purposes, including security, health, human identification, and law enforcement. Hence, there is an increasing research interest in automatic age estimation using biometric traits such as face and gait. In recent years, gait analysis has received growing attention due to the pervasive nature of video surveillance. Gait signals that measure the manner of walking can be obtained using vision and sensor-based techniques. Individual gait patterns obtainable from videos, images, or sensors are shown unconsciously and are not easily obscured. Additionally, gait signals can be obtained unobtrusively with cameras placed at a long distance because gait does not require high-resolution images. However, the extraction of age-associated gait features is a challenging task due to various gait covariates. These covariates include clothing and view changes for visionbased gait; walking slope and footwear for sensor-based gait. This paper provides a survey of scientific literature on age estimation gait features. We focus on the approaches to extracting age-associated gait features, namely, vision-based and sensor-based approaches, how they may be affected by the different covariates, and domain-specific applications. To make this work useful for as wide of an audience as possible, we also include discussions on key topics such as existing datasets, evaluation strategies, and open challenges that should be addressed in the future.

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“…where kernel scale σ is a constant to adjust the width of the Gaussian function. Replacing y k SV(i) , y k with the kernel function, the solution in (14) becomes…”

Section: Svrmentioning

confidence: 99%

“…For example, Davis [7] showed the gait difference between an adult and a child in terms of leg length, stride width, and stride frequencies. Ince et al [14] showed that the head-to-body ratio of a child is different from an adult. Similar findings were also reported in [25] by analyzing the most widely used appearance-based gait representation (known as gait energy images (GEIs) [13] or averaged silhouettes [18]), which contains both gait and shape information.…”

Section: Introductionmentioning

confidence: 99%

Gait-based human age estimation using age group-dependent manifold learning and regression

Makihara

et al. 2018

Multimed Tools Appl

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Human age estimation from gait is expected to be an important technology for a variety of applications such as automatic customer counting for marketing research or automatic age-based access control restriction for a specific area because the gait can be observable at a distance from a camera (e.g., CCTV). Although the aging process of gait significantly differs among age groups (e.g., children, adults, and the elderly), previous studies on gait-based human age estimation employ a single age group-independent estimation model that suffers from large estimation errors when the age variation increases. We therefore propose an age group-dependent gait-based human age estimation method for better accuracy. Specifically, in the training phase, we first compose age groups that are well-separated from each other by clustering gait features along with their age labels. We then learn a classifier that classifies the gait features for multiple age groups using a directed acyclic graph support vector machine. Next, we learn an age regression model for each age group using support vector regression with a Gaussian kernel in conjunction with a manifold learning technique, i.e., orthogonal locality preserving projection, to better

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Child and Adult Classification Using Ratio of Head and Body Heights in Images

Cited by 13 publications

References 6 publications

Face and silhouette based age estimation for child detection system

Face and silhouette based age estimation for child detection system

Learning Age From Gait: A Survey

Gait-based human age estimation using age group-dependent manifold learning and regression

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