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Extract Features from Periocular Region to Identify the Age Using Machine Learning Algorithms.

Latest studies done on huge data collected from aging features proved that the performance of facial image based age estimation is low and need to be improved. One of the significant biometric traits for human recognition or search is Human age. Age assessment is very much exigent over other pattern...

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Published in:Journal of Medical Systems Vol. 43; no. 7; pp. 1 - 16
Main Authors: Kamarajugadda, Kishore Kumar, Polipalli, Trinatha Rao
Format: equations & formulas pictorial tables/charts Journal Article
Published: Springer Nature Jul2019
Online Access:View this record in EBSCOhost
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      dt: Jul2019
      vid: 43
      iid: 7
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-019-1335-0
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        atl: Extract Features from Periocular Region to Identify the Age Using Machine Learning Algorithms.
      aug:
        au:
          Kamarajugadda, Kishore Kumar
          Polipalli, Trinatha Rao
        affil: Department of ECE, Faculty of Science and Technology, IFHE, Hyderabad, India
      sug:
        subj:
          Eye
          Face
          Aging Evaluation
          Machine Learning
          Algorithms Methods
          Conceptual Framework
          Facial Expression
          Photography
          Imaging, Three-Dimensional
          Digital Imaging
          Recognition (Psychology)
          Research, Medical
          Reports
      ab: Latest studies done on huge data collected from aging features proved that the performance of facial image based age estimation is low and need to be improved. One of the significant biometric traits for human recognition or search is Human age. Age assessment is very much exigent over other pattern recognition problems since the aging differs from person to person. This paper proposes a new framework that uses periocular region for age feature extraction and application of hybrid algorithm for age recognition. Firstly, preprocessing and periocular region normalization is done to acquire age invariant features. Secondly, the periocular region that underwent preprocessing is analyzed using hybrid approach, a novel machine algorithm that combines both SVM and kNN. The proposed technique generates the best recognition outputs.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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