Partial Face Recognition by plucking objects features and Dynamic Feature Matching

Authors: Shreya Gondchawar, Jyoti Gupta, Vandita Ahire, Pavan Jagadale, Dr.Y.B.Gurav

DOI: https://doi.org/10.17605/OSF.IO/47YAN

Short DOI: https://doi.org/ggkv9p

Country: -

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Abstract: Partial face identification (PFR) is a free setting could be an important task, particularly in things wherever partial face pictures area unit doubtless control by force because of the blockage, no longer visible, and huge viewing angle. This is study of proposes a completely unique partial face identification approach, known as Dynamic Feature Matching (DFM), which mixes totally Convolutional Networks (FCNs) and thin illustration Classification (SRC) to handle partial face recognition downside no matter numerous face sizes. DFM doesn't need previous position info of partial faces against a characterized face. By dividing computation, to map the feature area unit calculated from the whole input image once, that yields a major quicker.

Keywords: Machine learning, deep Convolutional neural network, classification

Paper Id: 611

Published On: 2019-11-09

Published In: Volume 7, Issue 6, November-December 2019

Cite This: Partial Face Recognition by plucking objects features and Dynamic Feature Matching - Shreya Gondchawar, Jyoti Gupta, Vandita Ahire, Pavan Jagadale, Dr.Y.B.Gurav - IJIRMPS Volume 7, Issue 6, November-December 2019. DOI 10.17605/OSF.IO/47YAN

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