Face Parts Recognition Using Deep Neural Networks

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dc.contributor.authorKrishna, M.S.
dc.contributor.authorNali, A.
dc.contributor.authorAggarwal, N.
dc.contributor.authorKrishna, T.
dc.contributor.authorRamesh, R.
dc.date.accessioned2026-02-06T06:36:06Z
dc.date.issued2021
dc.description.abstractThis paper has expressed overall procedure of the facial recognition with its importance and essential beneficial factors. CNN and ML methods are used to find out the accuracy of the model for which data test train and features extraction has been processed. The output accuracy is observed to be 91.8%. Involvement of optimizers, batch normalization and dropout functionalities reported advantages in proposed CNN model. © 2021 IEEE.
dc.identifier.citationProceedings of International Conference on Technological Advancements and Innovations, ICTAI 2021, 2021, Vol., , p. 653-658
dc.identifier.urihttps://doi.org/10.1109/ICTAI53825.2021.9673433
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/30254
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectbatch normalization
dc.subjectCNN
dc.subjectdata train test and split
dc.subjectfeatures extraction
dc.subjectML algorithms
dc.titleFace Parts Recognition Using Deep Neural Networks

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