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Age Estimation Using Fixed Rank Representation (FRR)

Rohini G. Bhaisare1 , S.S. Ponde2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-12 , Page no. 35-40, Dec-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i12.3540

Online published on Dec 31, 2019

Copyright © Rohini G. Bhaisare, S.S. Ponde . 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: Rohini G. Bhaisare, S.S. Ponde, “Age Estimation Using Fixed Rank Representation (FRR),” International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.35-40, 2019.

MLA Style Citation: Rohini G. Bhaisare, S.S. Ponde "Age Estimation Using Fixed Rank Representation (FRR)." International Journal of Computer Sciences and Engineering 7.12 (2019): 35-40.

APA Style Citation: Rohini G. Bhaisare, S.S. Ponde, (2019). Age Estimation Using Fixed Rank Representation (FRR). International Journal of Computer Sciences and Engineering, 7(12), 35-40.

BibTex Style Citation:
@article{Bhaisare_2019,
author = {Rohini G. Bhaisare, S.S. Ponde},
title = {Age Estimation Using Fixed Rank Representation (FRR)},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2019},
volume = {7},
Issue = {12},
month = {12},
year = {2019},
issn = {2347-2693},
pages = {35-40},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4970},
doi = {https://doi.org/10.26438/ijcse/v7i12.3540}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i12.3540}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4970
TI - Age Estimation Using Fixed Rank Representation (FRR)
T2 - International Journal of Computer Sciences and Engineering
AU - Rohini G. Bhaisare, S.S. Ponde
PY - 2019
DA - 2019/12/31
PB - IJCSE, Indore, INDIA
SP - 35-40
IS - 12
VL - 7
SN - 2347-2693
ER -

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Abstract

As it is an important and challenging problem in computer vision, face age estimation is typically cast as a classification or regression problem over a set of face samples with several ordinal age labels which have intrinsically cross-age correlations across adjacent age dimensions. As an outcome, these such correlations normally lead to age label ambiguities of face samples. Each face sample is associated with a latent label distribution that encodes the cross-age correlation information on label ambiguities. As we propose a totally data-driven distribution learning, approach to adaptively learn the latent label distributions. The proposed approach is capable of effectively discovering the intrinsic age distribution patterns for cross-age correlation analysis on the any prior assumptions on the forms of label distribution learning, this approach is able to flexible model of sample-specific context aware label distribution properties by solving a multi-task problem which jointly optimizes the tasks of age-label distribution learning and age prediction for individuals. Experimental outcomes demonstrate effectiveness of our approach.

Key-Words / Index Term

Age estimation, subspace learning, label distribution learning

References

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