Gait-based age estimation using multi-stage convolutional neural network

Sakata, Atsuya; Takemura, Noriko; Yagi, Yasushi

doi:10.1186/s41074-019-0054-2

Cited by 35 publications

(29 citation statements)

References 26 publications

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“…In training CNNs, even if the same data are given, different parts of the data can be learned depending on the task 40 . With CNNs, multi-task learning has become possible, and this method improves the accuracy of a target task by simultaneously learning targets and by recognizing related adjacent structures 41 . Thus, this characteristic of an AI-based age-group estimation model is a very useful aspect in forensic dental medicine that can generally be well applied across various people and age groups.…”

Section: Discussionmentioning

confidence: 99%

“…First, for a wide range of age determination (20 y intervals, three groups in total), the accuracy of the AI model was analyzed. The young adults' group, which had the largest number of samples, was further divided into three subgroups (ages 20-29, ages 30-39, and ages [40][41][42][43][44][45][46][47][48][49]. Therefore, we compared the results obtained by dividing the participants into three age groups, and the results obtained by subdividing the young adults into further groups, thereby generating a total of five age groups.…”

Section: Methodsmentioning

confidence: 99%

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Age-group determination of living individuals using first molar images based on artificial intelligence

Kim

Lee

Noh

et al. 2021

Sci Rep

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Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography. The data set consisted of four first molar images from the right and left sides of the maxilla and mandible of each of 1586 individuals across all age groups, which were extracted from their panoramic radiographs. The accuracy of the tooth-wise estimation was 89.05 to 90.27%. Performance accuracy was evaluated mainly using a majority voting system and area under curve (AUC) scores. The AUC scores ranged from 0.94 to 0.98 for all age groups, which indicates outstanding capacity. The learned features of CNNs were visualized as a heatmap, and revealed that CNNs focus on differentiated anatomical parameters, including tooth pulp, alveolar bone level, or interdental space, depending on the age and location of the tooth. With this, we provided a deeper understanding of the most informative regions distinguished by age groups. The prediction accuracy and heat map analyses support that this AI-based age-group determination model is plausible and useful.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Age-group determination of living individuals using first molar images based on artificial intelligence

Kim

Lee

Noh

et al. 2021

Sci Rep

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“…For example, Sakata et al [21] used DenseNet [37], which is a state-of-the-art deep neural network architecture, and validated its effectiveness in the gait-based age estimation task. In addition to age, other attributes, such as gender, age group, and/or identity, have been incorporated into multi-task learning frameworks [16], [26] and a multi-stage learning framework [38]. Moreover, a gait-based age estimation method that is robust against a carried object was proposed in [27].…”

Section: Gait-based Age Estimationmentioning

confidence: 99%

“…Two major output/ground-truth representations exist: a scalar value for regression-based methods [13], [14], [21], [23], [38] and a one-hot vector for classification-based methods [15]. Different from these approaches, in this paper, we incorporate the idea of label distribution [32], [40].…”

Section: Output Representationsmentioning

confidence: 99%

Uncertainty-Aware Gait-Based Age Estimation and its Applications

Sakata

Makihara

et al. 2021

IEEE Trans. Biom. Behav. Identity Sci.

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Gait-based age estimation is a key technique for many applications. It is well known that age estimation uncertainty is highly dependent on age (i.e., small for children and large for adults), and it is important to know the uncertainty for the above-mentioned applications. Therefore, we propose a method for uncertainty-aware gait-based age estimation by introducing a label distribution learning framework. Specifically, we design a network that takes an appearance-based gait feature as input and outputs discrete label distributions in the integer age domain. We then train the network to minimize a loss function, which is defined as the dissimilarity between the estimated age distribution and the ground-truth age distribution, in addition to the conventional mean absolute error for the estimated age. Additionally, we demonstrate that uncertainty-aware gait-based age estimation is beneficial for two applications: person search by age query and people counting by age group. Experiments on the world's largest gait database, OULP-Age, demonstrated that the proposed method can successfully represent age estimation uncertainty, and outperforms or is comparable with state-of-the-art methods in terms of age estimation accuracy. Moreover, we demonstrated the effectiveness of the uncertainty-aware framework in applications to person search and people counting through experiments on the database.

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“…In their group-dependent framework based on manifold, they achieved excellent results. In another work exploring multi-stage CNN by [49], video-based gait-based age was estimated. In this paper, they introduced CNN-based models for gender estimation method, age-group estimation, and age regression.…”

Section: Related Workmentioning

confidence: 99%

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

Ahad

Ngo

Antar

et al. 2020

Sensors

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Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams—for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network.

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Gait-based age estimation using multi-stage convolutional neural network

Cited by 35 publications

References 26 publications

Age-group determination of living individuals using first molar images based on artificial intelligence

Age-group determination of living individuals using first molar images based on artificial intelligence

Uncertainty-Aware Gait-Based Age Estimation and its Applications

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

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