Improved Gender Classification Using Nonpathological Gait Kinematics in Full-Motion Video

Flora, Jeffrey B.; Lochtefeld, Darrell F.; Bruening, Dustin A.; Iftekharuddin, Khan M.

doi:10.1109/thms.2015.2398732

Cited by 18 publications

(19 citation statements)

References 27 publications

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“…Current gender recognition techniques in literature have been applied to various types of data including those that are image-based [2][3][4] , audio-based [1,5,6] or gait-based [7][8][9][10][11] to name a few. Wu et al [12] categorize approaches into appearancebased approaches which include static-body, dynamic-body and apparel features in contrast to non-appearance based approaches which include data types such as speech, iris, voice and fingerprints.…”

Section: Data Capturementioning

confidence: 99%

“…Rahman et al [12] identify gender based on Bengali speech extracted from The Bengali Daily Newspaper. Another data format that is becoming more widely used for experimentation purposes pertains to a person's gait [7,8,9,[14][15][16][17][18][19][20] , which can be image-based or contain spatio-temporal marker data locations. Additionally, gender identification has been performed on handwriting data using gradient features in [21] .…”

Section: Data Capturementioning

confidence: 99%

“…Prior to feature extraction, various preprocessing steps are implemented in order to improve the quality of the data or to eliminate undesired information. Preprocessing techniques include those such as size normalization [7][8][9] silhouetting of images, and noise reduction. Maurer et al [25] addressed the issue of height in gender classification by dividing each initial marker position by the height of each the specific subject in the preprocessing process.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Popular methods used for gender classification include implementing a support vector machine (SVM) with various kernels or by means of AdaBoost combined with SVM [29] . Flora et al [7,8] perform gender classification using nonpathological gait kinematics of subjects from walking data [8] . Das et al [11] perform gender identification on gait features using a hidden markov model and SVM [11] .…”

Section: Pattern Classificationmentioning

confidence: 99%

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Gender classification of running subjects using full-body kinematics

2016

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This paper proposes novel automated gender classification of subjects while engaged in running activity. The machine learning techniques include preprocessing steps using principal component analysis followed by classification with linear discriminant analysis, and nonlinear support vector machines, and decision-stump with AdaBoost. The dataset consists of 49 subjects (25 males, 24 females, 2 trials each) all equipped with approximately 80 retroreflective markers. The trials are reflective of the subject's entire body moving unrestrained through a capture volume at a self-selected running speed, thus producing highly realistic data. The classification accuracy using leave-one-out cross validation for the 49 subjects is improved from 66.33% using linear discriminant analysis to 86.74% using the nonlinear support vector machine. Results are further improved to 87.76% by means of implementing a nonlinear decision stump with AdaBoost classifier. The experimental findings suggest that the linear classification approaches are inadequate in classifying gender for a large dataset with subjects running in a moderately uninhibited environment.

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Section: Data Capturementioning

confidence: 99%

Section: Data Capturementioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Pattern Classificationmentioning

confidence: 99%

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Gender classification of running subjects using full-body kinematics

2016

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“…For instance, it is well known that human beings can capture information about terrain during walking by sensing it with their feet and by the sound of their footsteps [22]. The kinematic properties of the human motion pattern allow capturing the motion data for gait analysis, which in turn has been used as a reliable source for activity recognition [23] and estimating soft biometrics including gait-based age estimation [24,25], gender classification [24,26], emotion recognition [27] and human authentication/identification [28,29]. Moreover, with the ubiquitous availability of modern devices such as smartphones and wearables are typically equipped with many sensors.…”

Section: Introductionmentioning

confidence: 99%

What Lies Beneath One’s Feet? Terrain Classification Using Inertial Data of Human Walk

et al. 2019

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The objective of this study was to investigate if the inertial data collected from normal human walk can be used to reveal the underlying terrain types. For this purpose, we recorded the gait patterns of normal human walk on six different terrain types with variation in hardness and friction using body mounted inertial sensors. We collected accelerations and angular velocities of 40 healthy subjects with two smartphones embedded inertial measurement units (MPU-6500) attached at two different body locations (chest and lower back). The recorded data were segmented with stride based segmentation approach and 194 tempo-spectral features were computed for each stride. We trained two machine learning classifiers, namely random forest and support vector machine, and cross validated the results with 10-fold cross-validation strategy. The classification tasks were performed on indoor–outdoor terrains, hard–soft terrains, and a combination of binary, ternary, quaternary, quinary and senary terrains. From the experimental results, the classification accuracies of 97% and 92% were achieved for indoor–outdoor and hard–soft terrains, respectively. The classification results for binary, ternary, quaternary, quinary and senary class classification were 96%, 94%, 92%, 90%, and 89%, respectively. These results demonstrate that the stride data collected with the low-level signals of a single IMU can be used to train classifiers and predict terrain types with high accuracy. Moreover, the problem at hand can be solved invariant of sensor type and sensor location.

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Analysis of Histogram of Oriented Gradients on Gait Recognition

Wattanapanich

Wei

2021

Pattern Recognition and Artificial Intelligence

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This study investigates the impact of Histogram of Oriented Gradients (HoG) on gait recognition. HoG is applied to four basic gait representations, i.e. Gait Energy Image (GEI), Gait Entropy Image (GEnI), Gait Gaussian Image (GGI), and newly developed Gait Gaussian Entropy Image (GGEnI). Hence their corresponding secondary gait representations, Gradient Histogram Gait Images (GHGI), are generated. Due to the nature of HoGs, the secondary gait representations contain rich information of images from different scales and orientations. The optimized HoG parameters are investigated to establish appropriate parameter settings in the HoG operations. Evaluations are conducted by using Support Vector Machines (SVM) as classifier swith CASIA dataset B. Experimental results have shown that HoG associated secondary representations are superior to the originally basic representations in gait recognition, especially in case of coping with appearance changes, such as walking with bag and walking with coat when using normal walking samples in training. GHGIs have increased the gait recognition rate approximately of 17% to GEI, 12% to GEnI, 24% to GGI and 20% to GGEnI.

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Improved Gender Classification Using Nonpathological Gait Kinematics in Full-Motion Video

Cited by 18 publications

References 27 publications

Gender classification of running subjects using full-body kinematics

Gender classification of running subjects using full-body kinematics

What Lies Beneath One’s Feet? Terrain Classification Using Inertial Data of Human Walk

Analysis of Histogram of Oriented Gradients on Gait Recognition

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