Human Gait Gender Classification in Spatial and Temporal Reasoning

Arai, Kohei; Asmara, Rosa Andrie

doi:10.14569/ijarai.2012.010601

Cited by 19 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experimental results shown in Table 1 illustrate that the methods were proposed in, [13], [14] and [21] used only single set from (31 males and 31 females) subject data. The similar method to what we presented, are proposed in [15] and [16], where10-fold cross validation used and the experiment repeated 10 times.…”

Section: B Classification Methodsmentioning

confidence: 99%

Human gait gender classification based on fusing spatio-temporal and wavelet statistical features

Sabir

Al-Jawad

Jassim

et al. 2013

2013 5th Computer Science and Electronic Engineering Conference (CEEC)

View full text Add to dashboard Cite

Gait recognition is one of the biometric recognition systems that do not require observed subject's attention and assistance. This paper proposes gender classification based on human gait. Gender is an important demographic attribute of people that can play a significant role in automatic gait recognition, the perception of gender determines social interactions. Humans are very accurate at recognizing gender from face, voice or the manner in which an individual walks. In our proposed technique we focus on using three different types of features; Spatio-Temporal Model, Leg Motion Detection, and Statistical Wavelet Model. These features have different characteristics to be used in gender recognition system based on gait recognition. For testing the performance of our method we used CASIA B gait database this paper proposes a way of testing the performance by selecting randomly equal subset of males and females then run the experiment repeatedly many times to cover the entire subjects in the database. This testing approach makes the achieved result more reliable compared with the existing approaches. Two different classification methods used in our proposal; K-Nearest Neighbors and Support Vector Machine. Our experimental results, of 96.47% classification rate in average, show that our approach is providing more trustworthy accuracy compared with the existent approaches.

show abstract

Section: B Classification Methodsmentioning

confidence: 99%

Human gait gender classification based on fusing spatio-temporal and wavelet statistical features

Sabir

Al-Jawad

Jassim

et al. 2013

2013 5th Computer Science and Electronic Engineering Conference (CEEC)

View full text Add to dashboard Cite

show abstract

“…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%

Untitled

2018

FSAR

View full text Add to dashboard Cite

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.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Human Gait Gender Classification in Spatial and Temporal Reasoning

Cited by 19 publications

References 22 publications

Human gait gender classification based on fusing spatio-temporal and wavelet statistical features

Human gait gender classification based on fusing spatio-temporal and wavelet statistical features

Untitled

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

Contact Info

Product

Resources

About