Race Classification through Convolutional Neural Network

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dc.contributor.authorTalha Mahboob Alam
dc.contributor.authorTalha Imtiaz Baig
dc.contributor.authorAbdul Wahab
dc.contributor.authorMalik Furqan Zahid
dc.date.accessioned2019-01-02T06:35:34Z
dc.date.available2019-01-02T06:35:34Z
dc.date.issued2017
dc.descriptionTayaba Anjumen_US
dc.description.abstractNeural networks are a powerful technology to classify different images. However, there are impressive number of different types of neural networks that are used in the literature and in industry but we used Convolutional neural network (CNN) to classify the images. The biometric framework can utilize race to distinguish individuals in the world with a precise personality. This research proposes a design to order individuals into two races Asian and Non-Asian that effectively in any face acknowledgment framework can be incorporated. The proposed procedure takes in the assigned essential characteristic of the face, skin color pattern and other secondary feature from training of images in order to effectively classify races. We use CNN to create a system that classifies facial images that are based on a variety of different facial attributes and classify it into two separate classes. We use 3 convolutional layers. We used 3052 training images of 64*64 pixels and we achieve 85% accuracy.en_US
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/3477
dc.language.isoenen_US
dc.publisherUniversity of Management & Technologyen_US
dc.subjectWeber Local Descriptor, Deep Boltzmann Machine, Local Binary Patternsen_US
dc.subjectBSen_US
dc.titleRace Classification through Convolutional Neural Networken_US
dc.titleRace classification through convolutional neural networken_us
dc.typeThesisen_US
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