Gender discrimination, age group classification and carried object recognition from gait energy image using fusion of parallel convolutional neural network

Russel, Newlin Shebiah; Arivazhagan, S.

doi:10.1049/ipr2.12024

Cited by 18 publications

(8 citation statements)

References 38 publications

(55 reference statements)

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“…The two models achieve high gender classification accuracy, even surpassing the CNN+SVM method. Russel and Selvaraj [30] proposed a complex gender classification framework containing six parallel CNNs with the input of GEIs. The parallel networks contain varying numbers of convolution layers.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Gender Recognition Based on Gradual and Ensemble Learning from Multi-View Gait Energy Images and Poses

Leung,

Chan

2023

Sensors

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Image-based gender classification is very useful in many applications, such as intelligent surveillance, micromarketing, etc. One common approach is to adopt a machine learning algorithm to recognize the gender class of the captured subject based on spatio-temporal gait features extracted from the image. The image input can be generated from the video of the walking cycle, e.g., gait energy image (GEI). Recognition accuracy depends on the similarity of intra-class GEIs, as well as the dissimilarity of inter-class GEIs. However, we observe that, at some viewing angles, the GEIs of both gender classes are very similar. Moreover, the GEI does not exhibit a clear appearance of posture. We postulate that distinctive postures of the walking cycle can provide additional and valuable information for gender classification. This paper proposes a gender classification framework that exploits multiple inputs of the GEI and the characteristic poses of the walking cycle. The proposed framework is a cascade network that is capable of gradually learning the gait features from images acquired in multiple views. The cascade network contains a feature extractor and gender classifier. The multi-stream feature extractor network is trained to extract features from the multiple input images. Features are then fed to the classifier network, which is trained with ensemble learning. We evaluate and compare the performance of our proposed framework with state-of-the-art gait-based gender classification methods on benchmark datasets. The proposed framework outperforms other methods that only utilize a single input of the GEI or pose.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…Temporal information, characterized by the optical flow map, is then computed from two adjacent subGEIs. Russel and Selvaraj [30] proposed a unified model with the input of GEIs to six parallel CNNs. The parallel network contains a varying number of convolution layers.…”

Section: Deep Learning Modelmentioning

confidence: 99%

Gender Recognition Based on Gradual and Ensemble Learning from Multi-View Gait Energy Images and Poses

Leung,

Chan

2023

Sensors

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“…Using a deep residual network, Zhang et al [77] performed a multi-task classification of subjects based on age and gender. The results obtained by [77]- [79] suggest that multi-task learning can improve the accuracy of age estimation. For example, learning age and gender in parallel can improve the accuracy of age estimation.…”

Section: ) Gait Age From Model-free Featuresmentioning

confidence: 93%

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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“…Gender classification is performed in a number of ways using gait. According to the format of the gait, the existing gait identification techniques can be classified into 2D-based and 3D-based techniques [ 27 , 28 , 29 ]. The 2D-based gait identification techniques depend on a human silhouette, and is often collected by a single RGB camera in video surveillance.…”

Section: Related Workmentioning

confidence: 99%

A Gait-Based Real-Time Gender Classification System Using Whole Body Joints

Azhar

Ullah

et al. 2022

Sensors

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Gait-based gender classification is a challenging task since people may walk in different directions with varying speed, gait style, and occluded joints. The majority of research studies in the literature focused on gender-specific joints, while there is less attention on the comparison of all of a body’s joints. To consider all of the joints, it is essential to determine a person’s gender based on their gait using a Kinect sensor. This paper proposes a logistic-regression-based machine learning model using whole body joints for gender classification. The proposed method consists of different phases including gait feature extraction based on three dimensional (3D) positions, feature selection, and classification of human gender. The Kinect sensor is used to extract 3D features of different joints. Different statistical tools such as Cronbach’s alpha, correlation, t-test, and ANOVA techniques are exploited to select significant joints. The Coronbach’s alpha technique yields an average result of 99.74%, which indicates the reliability of joints. Similarly, the correlation results indicate that there is significant difference between male and female joints during gait. t-test and ANOVA approaches demonstrate that all twenty joints are statistically significant for gender classification, because the p-value for each joint is zero and less than 1%. Finally, classification is performed based on the selected features using binary logistic regression model. A total of hundred (100) volunteers participated in the experiments in real scenario. The suggested method successfully classifies gender based on 3D features recorded in real-time using machine learning classifier with an accuracy of 98.0% using all body joints. The proposed method outperformed the existing systems which mostly rely on digital images.

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Gender discrimination, age group classification and carried object recognition from gait energy image using fusion of parallel convolutional neural network

Cited by 18 publications

References 38 publications

Gender Recognition Based on Gradual and Ensemble Learning from Multi-View Gait Energy Images and Poses

Gender Recognition Based on Gradual and Ensemble Learning from Multi-View Gait Energy Images and Poses

Learning Age From Gait: A Survey

A Gait-Based Real-Time Gender Classification System Using Whole Body Joints

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