Human Gait Gender Classification using 3D Discrete Wavelet Transform Feature Extraction

Arai, Kohei; Asmara, Rosa Andrie

doi:10.14569/ijarai.2014.030203

Cited by 17 publications

(20 citation statements)

References 15 publications

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“…In general, it is believed that male and female have unique physical characteristics, such as fingerprint, palmprint, and face. Even in [1,2] writers reported that male and female shows the difference in the way they talk or walk. When gender can be detected based on their physical characteristics, unknown criminal or victims of natural disasters can be identified faster.…”

Section: Literature Reviewsmentioning

confidence: 99%

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2018

FSAR

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Violent criminal activities under most circumstances are accompanied by large spillage of blood. A bloodstain formed due to accumulation of liquid blood on an absorbent surface such as carpet, fabric, clothing, bed sheet, curtains etc. is commonly referred to as 'Saturation Stain'. This paper proposed a computer based system that could effectively predict the gender of an individual using SVM basic kernel functions.

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Section: Literature Reviewsmentioning

confidence: 99%

Untitled

2018

FSAR

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“…In general, it is believed that male and female have unique physical characteristics, such as fingerprint, palmprint, and face. Even in [1] and [2] writers reported that male and female shows the difference in the way they talk or walk. When gender can be detected based on their physical characteristics, unknown criminal or victims of natural disasters can be identified faster.…”

Section: Introductionmentioning

confidence: 99%

A Short Review of Gender Classification based on Fingerprint using Wavelet Transform

Suwarno¹,

Santosa²

2017

ijacsa

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Abstract-In some cases, knowing the gender of fingerprint owner found in criminal or disaster scene is advantageous. Theoretically, if the number of the male and female fingerprints in a database is equal, then the identification process of a fingerprint on that database would be two times faster. Some methods have been used to classify gender based on the fingerprint. Most of the method is based on ridge density. This method showed good result. However, it is sensitive to the location of the fingerprint area where ridge density is determined. This paper reviews some literature that used wavelet transform to generate features of a fingerprint. As far as what we found in literature, the number of papers on this topic is very limited. However, based on the literature we reviewed, wavelet transform gives some advantages compared to ridge density counting.

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“…Many researchers had focused this issue to consider for a new recognition system [4][5][6][7][8][9][10][11]. Human Gait classification and recognition giving some advantage compared to other recognition system.…”

Section: Introductionmentioning

confidence: 99%

3D Skeleton model derived from Kinect Depth Sensor Camera and its application to walking style quality evaluations

Arai¹,

Asmara²

2013

IJARAI

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Abstract-Feature extraction for gait recognition has been created widely. The ancestor for this task is divided into two parts, model based and free-model based. Model-based approaches obtain a set of static or dynamic skeleton parameters via modeling or tracking body components such as limbs, legs, arms and thighs. Model-free approaches focus on shapes of silhouettes or the entire movement of physical bodies. Model-free approaches are insensitive to the quality of silhouettes. Its advantage is a low computational costs comparing to modelbased approaches. However, they are usually not robust to viewpoints and scale. Imaging technology also developed quickly this decades. Motion capture (mocap) device integrated with motion sensor has an expensive price and can only be owned by big animation studio. Fortunately now already existed Kinect camera equipped with depth sensor image in the market with very low price compare to any mocap device. Of course the accuracy not as good as the expensive one, but using some preprocessing we can remove the jittery and noisy in the 3D skeleton points. Our proposed method is part of model based feature extraction and we call it 3D Skeleton model. 3D skeleton model for extracting gait itself is a new model style considering all the previous model is using 2D skeleton model. The advantages itself is getting accurate coordinate of 3D point for each skeleton model rather than only 2D point. We use Kinect to get the depth data. We use Ipisoft mocap software to extract 3d skeleton model from Kinect video. From the experimental results shows 86.36% correctly classified instances using SVM.

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Human Gait Gender Classification using 3D Discrete Wavelet Transform Feature Extraction

Cited by 17 publications

References 15 publications

Untitled

Untitled

A Short Review of Gender Classification based on Fingerprint using Wavelet Transform

3D Skeleton model derived from Kinect Depth Sensor Camera and its application to walking style quality evaluations

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