Fine-Grained Age Group Classification in the wild

Zhang, Ke; Liu, Na; Yuan, Xingfang; Guo, Xinyao; Gao, Ce; Zhao, Zhenbing

doi:10.1109/icpr.2018.8545333

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…In previous studies, gender prediction on smart devices often relied on voice recordings and facial images [24], [25]. Furthermore, studies in the literature explore age and gender estimation using image-based gait information [26]- [28]. For instance, the authors investigated the application of computer vision and gait analysis in gender classification for forensics in [28].…”

Section: Literature Overviewmentioning

confidence: 99%

Gender Detection Based on Gait Data: A Deep Learning Approach With Synthetic Data Generation and Continuous Wavelet Transform

Davarci,

Anarim

2023

IEEE Access

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Smart devices equipped with various sensors enable the acquisition of users' behavioral biometrics. These sensor data capture variations in users' interactions with the devices, which can be analyzed to extract valuable information such as user activity, age group, and gender. In this study, we investigate the feasibility of using gait data for gender detection of users. To achieve this, we propose a novel gender detection scheme based on a deep learning approach, incorporating synthetic data generation and continuous wavelet transform (CWT). In this scheme, the real dataset is first divided into training and test datasets, and then synthetic data are intelligently generated using various techniques to augment the existing training data. Subsequently, CWT is used as the feature extraction module, and its outputs are fed into a deep learning model to detect the gender of users. Different deep learning models, including convolutional neural network (CNN) and long short-term memory (LSTM), are employed in classification. Consequently, we evaluate our proposed framework on different publicly available datasets. On the BOUN Sensor dataset, we obtain an accuracy of 94.83%, marking a substantial 6.5% enhancement over the prior highest rate of 88.33%. Additionally, we achieve 86.27% and 88.15% accuracy on the OU-ISIR Android and OU-ISIR Center IMUZ datasets, respectively. Our experimental results demonstrate that our proposed model achieves high detection rates and outperforms previous methods across all datasets. INDEX TERMSBiometrics, continuous wavelet transform, convolutional neural networks, frequency domain, gender detection, generative adversarial networks, human gait, motion sensors, smartphones. authors successfully determined user gender by analyzing smartphone accelerometer sensor data. Accelerometer sen-sor data have also been leveraged to detect behaviors associated with individuals' stress levels in [2]. Additionally, smartphone motion sensor data has been utilized in multiple studies to recognize and classify human daily activities [3]-[5]. On the other hand, the authors have employed sensor data from smart devices to enhance user identification and authentication systems in [6], [7].Gait analysis is a significant biometric feature that facilitates human identification and provides insights into physical and medical conditions. Due to the unique nature of an individual's gait, which reflects their walking style and physical abilities, it becomes challenging to mimic the gait pattern of others [8]. Thus, gait analysis finds applications across various domains, including security, sports, surveillance, and the medical field [9], [10]. For instance, in [11], the authors underscored using sensor-based gait analysis in clinical applications for monitoring and diagnosing conditions such as Parkinson's disease.Sensor data originating from touchscreen interactions and

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Section: Literature Overviewmentioning

confidence: 99%

Gender Detection Based on Gait Data: A Deep Learning Approach With Synthetic Data Generation and Continuous Wavelet Transform

Davarci,

Anarim

2023

IEEE Access

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“…More recent work by Zhang et al. [29,30,31] also explored the elicitation of age and gender from image-based gait information. They propose, amongst others, a convolutional neural network (CNN)-based method for age group and gender estimation that leverages residual networks of residual networks (RoR).…”

Section: Related Workmentioning

confidence: 99%

A Systematic Comparison of Age and Gender Prediction on IMU Sensor-Based Gait Traces

hamme

Garofalo

Rúa

et al. 2019

Sensors

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Sensors provide the foundation of many smart applications and cyber–physical systems by measuring and processing information upon which applications can make intelligent decisions or inform their users. Inertial measurement unit (IMU) sensors—and accelerometers and gyroscopes in particular—are readily available on contemporary smartphones and wearable devices. They have been widely adopted in the area of activity recognition, with fall detection and step counting applications being prominent examples in this field. However, these sensors may also incidentally reveal sensitive information in a way that is not easily envisioned upfront by developers. Far worse, the leakage of sensitive information to third parties, such as recommender systems or targeted advertising applications, may cause privacy concerns for unsuspecting end-users. In this paper, we explore the elicitation of age and gender information from gait traces obtained from IMU sensors, and systematically compare different feature engineering and machine learning algorithms, including both traditional and deep learning methods. We describe in detail the prediction methods that our team used in the OU-ISIR Wearable Sensor-based Gait Challenge: Age and Gender (GAG 2019) at the 12th IAPR International Conference on Biometrics. In these two competitions, our team obtained the best solutions amongst all international participants, and this for both the age and gender predictions. Our research shows that it is feasible to predict age and gender with a reasonable accuracy on gait traces of just a few seconds. Furthermore, it illustrates the need to put in place adequate measures in order to mitigate unintended information leakage by abusing sensors as an unanticipated side channel for sensitive information or private traits.

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Discriminative fine-grained network for vehicle re-identification using two-stage re-ranking

Wang

Min

et al. 2020

Sci. China Inf. Sci.

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Fine-Grained Age Group Classification in the wild

Cited by 5 publications

References 14 publications

Gender Detection Based on Gait Data: A Deep Learning Approach With Synthetic Data Generation and Continuous Wavelet Transform

Gender Detection Based on Gait Data: A Deep Learning Approach With Synthetic Data Generation and Continuous Wavelet Transform

A Systematic Comparison of Age and Gender Prediction on IMU Sensor-Based Gait Traces

Discriminative fine-grained network for vehicle re-identification using two-stage re-ranking

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