Gender & Age Detection using CNN and Deep Learning

Simple item page
dc.contributor.advisorDr. Shahnewaz Siddique
dc.contributor.authorLamia Akter Shahinur
dc.contributor.authorMahmuda Akter Meem
dc.contributor.authorKaniz Fatema
dc.contributor.authorMd. Mobasshir Miah
dc.contributor.id1821023042
dc.contributor.id1821029042
dc.contributor.id1821382042
dc.contributor.id1911248642
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2024-06-05
dc.date.accessioned2024-06-05T04:14:28Z
dc.date.available2024-06-05T04:14:28Z
dc.date.issued2022
dc.description.abstractAn application for video data analysis based on computer vision and machine learning method are presented. Novel gender and age classifiers based on adaptive features, local binary patterns and support vector machines are proposed. As features for the gender and age estimation, facial shape, skin texture, hue and Gabor feature are used. In order to show the effectiveness of proposed method, not only real-age database of facial image but also appearance-age database is employed. We also analyze the facial features characteristic to each age category and gender, and examine the difference feature of between the real-age and appearance-age in a facial area. Moreover, we examined the left-right symmetric property of the face concerning gender and age estimation by the proposed method. The promising practical application of such algorithms can be human-computer interaction, surveillance monitoring, video content analysis, targeted advertising, biometrics, and entertainment.
dc.description.degreeUndergraduate
dc.identifier.cd600000086
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/893
dc.language.isoen_US
dc.publisherNorth South University
dc.rights© NSU Library
dc.subjectTECHNOLOGY::Electrical engineering, electronics and photonics::Electrical engineering
dc.titleGender & Age Detection using CNN and Deep Learning
dc.typeProject
oaire.citation.endPage40
oaire.citation.startPage1
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
600000086-abstract.pdf
Size:
128.16 KB
Format:
Adobe Portable Document Format
Description:
Download
Thumbnail Image
Name:
600000086.pdf
Size:
1.21 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.93 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
Theses - Undergraduate