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

Li, Xiang; Makihara, Yasushi; Xu, Chi; Yagi, Yasushi; Ren, Mengye

doi:10.1007/s11042-018-6049-7

Cited by 46 publications

(32 citation statements)

References 38 publications

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“…For example, ref. [48] proposed a method for age estimation and experimented on the OU-ISIR Gait Database, Large Population Dataset with Age (OULP-Age). This dataset had more than 60,000 participants and age ranges from 2 to 90 years old.…”

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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Section: Related Workmentioning

confidence: 99%

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

Ahad

Ngo

Antar

et al. 2020

Sensors

Self Cite

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“…proposed an age group-dependent manifold method [14]. After an age group classifier has been trained, a kernel SVM regression was added for accurately assessment in each age group.…”

Section: Related Workmentioning

confidence: 99%

“…This method proposed in [23] achieves an outperform performance in the field of face-based age estimation. Methods MAE CS (k = 5) SVR [34] 7.66 41.40% MLG [15] 10.98 43.40% OPLDA [16] 8.45 36.50% OPMFA [16] 9.08 34.70% GPR [21] 7.30 43.60% ASSOLPP [14] 6.78 53.00% VGG16 + Mean-Variance [23] 5.59 60.46% ODR-GLCNN (Ours) 5.12 66.95% Table 1. Comparisons of the age estimation MAEs by the proposed approach and the state-of-the-art methods on the OULP-Age dataset.…”

Section: Comparisons With the State-of-the-artsmentioning

confidence: 99%

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Ordinal distribution regression for gait-based age estimation

Zhu

Zhang

et al. 2020

Sci. China Inf. Sci.

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Computer vision researchers prefer to estimate the age from face images due to informative facial features. Estimating the age from face images becomes challenging when people are far away from camcorders or occluded. As the unique biometric feature that can be perceived efficiently at a distance, gait can be an alternative way to predict the age in case that face images are not available. However, existing gait-based classification or regression methods ignore the ordinal relationship of different ages, which is an important clue to the age estimation. In this paper, we propose an ordinal distribution regression with a global and local convolutional neural network for gait-based age estimation. Specifically, we decompose the gait-based age regression into a series of binary classifications to incorporate the ordinal information of the age. Then an ordinal distribution loss is proposed to take inner relationship among these classifications into account by penalizing the distribution discrepancy between the estimated and the groundtruth. In addition, our neural network consists of a global and three local sub-networks, which is capable of learning the global structure and more local details from head, body and feet of gait, respectively. By comparing with the stateof-the-art methods of gait-based age estimation, this paper highlights, experimentally, that the proposed approach has a better predictive performance on the OULP-Age dataset.

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“…The increase in age variation leads to large estimation error in independent age groups. A human age estimation technique that is dependent on age-group using support vector machines and regression model was proposed by Li et al [43] for improving the accuracy of the system. In-depth analysis of previous research has shown that Re-id is an important area that has been well explored, however, various challenges in the vision-based method limit the potential of the application.…”

Section: Literature Reviewmentioning

confidence: 99%

Person Re-Identification Using Deep Modeling of Temporally Correlated Inertial Motion Patterns

Gohar

Riaz

Shahzad

et al. 2020

Sensors

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Person re-identification (re-ID) is among the essential components that play an integral role in constituting an automated surveillance environment. Majorly, the problem is tackled using data acquired from vision sensors using appearance-based features, which are strongly dependent on visual cues such as color, texture, etc., consequently limiting the precise re-identification of an individual. To overcome such strong dependence on visual features, many researchers have tackled the re-identification problem using human gait, which is believed to be unique and provide a distinctive biometric signature that is particularly suitable for re-ID in uncontrolled environments. However, image-based gait analysis often fails to extract quality measurements of an individual’s motion patterns owing to problems related to variations in viewpoint, illumination (daylight), clothing, worn accessories, etc. To this end, in contrast to relying on image-based motion measurement, this paper demonstrates the potential to re-identify an individual using inertial measurements units (IMU) based on two common sensors, namely gyroscope and accelerometer. The experiment was carried out over data acquired using smartphones and wearable IMUs from a total of 86 randomly selected individuals including 49 males and 37 females between the ages of 17 and 72 years. The data signals were first segmented into single steps and strides, which were separately fed to train a sequential deep recurrent neural network to capture implicit arbitrary long-term temporal dependencies. The experimental setup was devised in a fashion to train the network on all the subjects using data related to half of the step and stride sequences only while the inference was performed on the remaining half for the purpose of re-identification. The obtained experimental results demonstrate the potential to reliably and accurately re-identify an individual based on one’s inertial sensor data.

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

Cited by 46 publications

References 38 publications

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

Ordinal distribution regression for gait-based age estimation

Person Re-Identification Using Deep Modeling of Temporally Correlated Inertial Motion Patterns

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