Age Estimation Using Fixed Rank Representation (FRR)

Authors

  • Bhaisare RG Dept. of Computer Science and Engineering, Deogiri Institute of Engineering & Management Studies, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India
  • SS Ponde Dept. of Computer Science and Engineering, Deogiri Institute of Engineering & Management Studies, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India

DOI:

https://doi.org/10.26438/ijcse/v7i12.3540

Keywords:

Age estimation, subspace learning, label distribution learning

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.

References

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Published

2019-12-31
CITATION
DOI: 10.26438/ijcse/v7i12.3540
Published: 2019-12-31

How to Cite

[1]
R. G. Bhaisare and P. SS, “Age Estimation Using Fixed Rank Representation (FRR)”, Int. J. Comp. Sci. Eng., vol. 7, no. 12, pp. 35–40, Dec. 2019.

Issue

Vol. 7 No. 12 (2019): IJCSE December Edition

Section

Research Article

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Creative Commons License

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