A Comparison Study of Face, Gait and Speech Features for Age Estimation

Punyani, Prachi; Gupta, Rashmi; Kumar, Ashwani

doi:10.1007/978-981-10-4765-7_34

Cited by 5 publications

(5 citation statements)

References 19 publications

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“…Despite the fact that our approach has certain limitations, the average age predication error is much lower than that of the existing methodologies [4], [34], [53]. It will be interesting to have longitudinal studies over several years or even decades.…”

Section: Discussionmentioning

confidence: 97%

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Move Your Body: Age Estimation Based on Chest Movement During Normal Walk

Riaz

Hashmi

et al. 2019

IEEE Access

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The estimation of soft biometrics of a subject, including age, through the gait analysis is a challenging area of research due to variations in individuals' gaits and the effect of ageing on gait patterns. In this paper, we present the results of age estimation based on the analysis of inertial data of human walk. We have recorded 6D accelerations and angular velocities of 86 subjects while performing standardized gait tasks using chest-mounted inertial measurement units. The recorded data were segmented to decompose the long sequences of signals into single steps. For each step, we compute a total of 50 spatio-spectral features from 6D components. We trained three different machine learning classifiers-random forests, support vector machines, and multi-layer perceptron-to estimate the human age. Two different types of cross validation strategies, i.e., tenfold and subjectwise cross validation were employed to gauge the performance of the estimators. The results reveal that it is possible to predict the age of a subject with higher accuracy. With a random forest regressor, when trained and validated on hybrid data, we achieved an average root mean square error of 3.32 years and a mean absolute error of 1.75 years under tenfold cross validation and average root mean square error of 8.22 years under subjectwise cross validation. Since our participants belong to two different demographical regions, i.e., Europe and South Asia, we confirm on broader empirical basis previous findings that age information is present in the human gait. Our proposed approach allows rather robust estimations of age based on the inertial data of a single step, as the used data consist of those collected on different ground surfaces, and the participants were also told to walk pretending different emotional states. The findings on the existing data point out the change of gait while aging, which will also imply that person identification using the gait depends on data that is not too old. INDEX TERMS Age estimation, inertial sensor based age estimation, human gait analysis, smartphone and wearable, soft biometrics.

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Section: Discussionmentioning

confidence: 97%

“…Riaz et al [4] proposed a method of estimating human soft biometrics including gender, age, and height from gait data and report RMSE of 11.51 years. Punyani et al [53] used a feature set comprised of facial, gait and speech features to estimate person age. From facial features, they report an MAE of 5.36 years.…”

Section: B Comparison With Existing Approachesmentioning

confidence: 99%

Move Your Body: Age Estimation Based on Chest Movement During Normal Walk

Riaz

Hashmi

et al. 2019

IEEE Access

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“…As shown in Table I, the related reviews on gait-based age estimation [21]- [23] have coverage only up to 2019 and present general overviews on age and gender estimation using gait features. In this paper, we aim to fill this gap by providing a comprehensive survey of scientific literature on age estimation using gait features from 2001 to 2021.…”

Section: Table I Related Reviews On Learning Age From Gaitmentioning

confidence: 99%

“…Hediyeh et al [61] explored step frequency and step length obtained automatically in uncontrolled environments for age and gender classification, achieving an accuracy of 86% for age classification. In a study to compare the performance of face, gait, and speech features, Punyani et al [21] combined gait speed, head-to-body ratio, and gait height to perform age estimation.…”

Section: Biological Featuresmentioning

confidence: 99%

“…Hence, most model-based approaches use conventional machine learning approaches for prediction and perform only age group classification (see Table V). Only Punyani et al [21] performed regression using model-based features. The main advantage of model-based features over model-free features is the greater interpretability of age prediction results.…”

Section: Suitability Of Gait Age Featuresmentioning

confidence: 99%

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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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Attention‐aware spatio‐temporal learning for multi‐view gait‐based age estimation and gender classification

Huang

Luo

Xie

et al. 2022

IET Computer Vision

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Recently, gait‐based age and gender recognition have attracted considerable attention in the fields of advertisement marketing and surveillance retrieval due to the unique advantage that gaits can be perceived at a long distance. Intuitively, age and gender can be recognised by observing people's static shape (e.g. different hairstyles between males and females) and dynamic motion (e.g. different walking velocities between the elderly and youth). However, most of the existing gait‐based age and gender recognition methods are based on Gait Energy Image (GEI), which loses the capability of explicitly modelling temporal dynamic information and is not robust to the multi‐view recognition that inevitably happens in a real application. Therefore, in this study, an Attention‐aware Spatio‐Temporal Learning (ASTL) framework is proposed, which employs a silhouette sequence as input to learn essential and invariable spatial‐temporal gait representations. More specifically, a Multi‐Scale Temporal Aggregation (MSTA) module provides an effective scheme for dynamic gait description by exploring and aggregating multi‐scale temporal interval information, which is a core supplement to spatial representation. Then, a Multiple Attention Aggregation (MAA) module is designed to help the network focus on the most discriminatory information along temporal, spatial and channel dimensions. Finally, a Multimodal Collaborative Learning (MCL) block gives full play to the advantages of different modal features through a multimodal cooperative learning strategy. The mean absolute error (MAE) for the age estimation and the correct classification rate (CCR) for the gender classification on OU‐MVLP achieve 6.68 years and 97%, respectively, demonstrating the superiority of the method. Ablation experiments and visualisation results also prove the effectiveness of the three individual modules in their framework.

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A Comparison Study of Face, Gait and Speech Features for Age Estimation

Cited by 5 publications

References 19 publications

Move Your Body: Age Estimation Based on Chest Movement During Normal Walk

Move Your Body: Age Estimation Based on Chest Movement During Normal Walk

Learning Age From Gait: A Survey

Attention‐aware spatio‐temporal learning for multi‐view gait‐based age estimation and gender classification

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