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...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 7; pp. 1 - 16 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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. |
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