Video Face Recognization Using Autoencoder and Softmax Classifications

Authors

  • Koganti S Dept of CSE, VNR Vignana Jyothi Institute of Engineering and Technology, JNTUH, Hyderabad, India
  • Kumar TS Dept of CSE, VNR Vignana Jyothi Institute of Engineering and Technology, JNTUH, Hyderabad, India

DOI:

https://doi.org/10.26438/ijcse/v7i6.491496

Keywords:

Face Verification, Neural Networks, DBC, YouTube, Tiny videos

Abstract

Abundance and obtainability of audiovisual capturing devices, like mobile phones and loop camera, have prompted analysis in videocassette face appearance perseption, that is extraordinarily relevant in impostion solicitations .While this methodologies are declared high precisions at equivalent error rates, enactment at lesser lying acceptance rates wants significant development. So, we tend to introduced a completely unique face verification rule, 1st the feature-rich frames are designated from a video sequence .Frame choice done by illustration learning-based feature extraction, is finished by using: 1) deep learning, combining of stacked demising distributed auto-encoder 2) deep Boltzmann classifier (DBC) 3) apprising the loss purpose of DBC by as well as distributed and short rank regularization. Finally, the results verified on 2 wide conferred databases, YouTube and little videos and Shoot Challenge.

References

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Published

2019-06-30
CITATION
DOI: 10.26438/ijcse/v7i6.491496
Published: 2019-06-30

How to Cite

[1]
S. Koganti and T. S. Kumar, “Video Face Recognization Using Autoencoder and Softmax Classifications”, Int. J. Comp. Sci. Eng., vol. 7, no. 6, pp. 491–496, Jun. 2019.

Issue

Vol. 7 No. 6 (2019): IJCSE June Edition

Section

Research Article

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Creative Commons License

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