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Enhanced deep learning with featured transfer learning in Identifying disguised faces

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dc.contributor.author Rao Raghava, Yedavalli Venkata
dc.contributor.author Kuthadi, Venu Madhav
dc.contributor.author Selvaraj, Rajalakshmi
dc.date.accessioned 2021-07-09T11:27:14Z
dc.date.available 2021-07-09T11:27:14Z
dc.date.issued 2019-08
dc.identifier.citation Rao Raghava ,Y. V., Kuthadi, M. V. and Selvaraj, R. (2019) Enhanced deep learning with featured transfer learning in identifying disguised faces, International Journal of Innovative Technology and Exploring Engineering , 8, (10), 1257-1260. 10.35940/ijitee.H7286.0881019 en_US
dc.identifier.issn 2278-3075
dc.identifier.uri http://repository.biust.ac.bw/handle/123456789/305
dc.description.abstract The objective of face recognition is, given an image of a human face identify the class to which the face belongs to. Face classification is one of the useful task and can be used as a base for many real-time applications like authentication, tracking, fraud detection etc. Given a photo of a person, we humans can easily identify who the person is without any effort. But manual systems are biased and involves lot of effort and expensive. Automatic face recognition has been an important research topic due to its importance in real-time applications. The recent advance in GPU has taken many applications like image classification, hand written digit recognition and object recognition to the next level. According to the literature Deep CNN (Convolution neural network) features can effectively represent the image. In this paper we propose to use deep CNN based features for face recognition task. In this work we also investigate the effectiveness of different Deep CNN models for the task of face recognition. Initially facial features are extracted from pretrained CNN model like VGG16, VGG19, ResNet50 and Inception V3. Then a deep Neural network is used for the classification task. To show the effectiveness of the proposed model, ORL dataset is used for our experimental studies. Based on the experimental results we claim that deep CNN based features give better performance than existing hand crafted features. We also observe that the among all the pretrained CNN models we used, ResNet scores highest performance. en_US
dc.language.iso en en_US
dc.publisher Blue Eyes Intelligence Engineering & Sciences Publication en_US
dc.subject Deep CNN features en_US
dc.subject CNN en_US
dc.subject DNN en_US
dc.subject Pre-training en_US
dc.subject Transfer learning en_US
dc.title Enhanced deep learning with featured transfer learning in Identifying disguised faces en_US
dc.description.level phd en_US
dc.description.accessibility unrestricted en_US
dc.description.department cis en_US


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