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Video Face Recognization Using Autoencoder and Softmax Classifications

Sonika Koganti1 , Talluri Sunil Kumar2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-6 , Page no. 491-496, Jun-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i6.491496

Online published on Jun 30, 2019

Copyright © Sonika Koganti, Talluri Sunil Kumar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Sonika Koganti, Talluri Sunil Kumar, “Video Face Recognization Using Autoencoder and Softmax Classifications,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.491-496, 2019.

MLA Style Citation: Sonika Koganti, Talluri Sunil Kumar "Video Face Recognization Using Autoencoder and Softmax Classifications." International Journal of Computer Sciences and Engineering 7.6 (2019): 491-496.

APA Style Citation: Sonika Koganti, Talluri Sunil Kumar, (2019). Video Face Recognization Using Autoencoder and Softmax Classifications. International Journal of Computer Sciences and Engineering, 7(6), 491-496.

BibTex Style Citation:
@article{Koganti_2019,
author = {Sonika Koganti, Talluri Sunil Kumar},
title = {Video Face Recognization Using Autoencoder and Softmax Classifications},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2019},
volume = {7},
Issue = {6},
month = {6},
year = {2019},
issn = {2347-2693},
pages = {491-496},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4578},
doi = {https://doi.org/10.26438/ijcse/v7i6.491496}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i6.491496}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4578
TI - Video Face Recognization Using Autoencoder and Softmax Classifications
T2 - International Journal of Computer Sciences and Engineering
AU - Sonika Koganti, Talluri Sunil Kumar
PY - 2019
DA - 2019/06/30
PB - IJCSE, Indore, INDIA
SP - 491-496
IS - 6
VL - 7
SN - 2347-2693
ER -

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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.

Key-Words / Index Term

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

References

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