Human Age Estimation Through Face Synthesis : A Survey

Panicker, Sreejit; Selot, Smita; Шарма, Маниша

doi:10.26634/jpr.1.2.2922

Cited by 2 publications

(1 citation statement)

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“…Thus, wrinkles analyses at different facial locations contribute to a more robust model for age classification. Age estimation through facial images still has its relevance in computer vision and pattern recognition [1][2] [3]. Major changes that happen in the human face in terms of aging is while transforming from youth to adulthood.…”

Section: Introductionmentioning

confidence: 99%

Improving Accuracy in Human Age Classification Using Ensemble Learning Techniques

Panicker

Selot

Шарма

2019

eijs

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Age is a predominant parameter for arbitrating an individual, for security and access concerns of the data that exist in cyber space. Nowadays we find a rapid growth in unethical practices from youngsters as well as skilled cyber users. Facial image renders a variety of information that can be used, when processed to ascertain the age of individuals. In this paper, local facial features are considered to predict the age group, where local Binary Pattern (LBP) is extracted from four regions of facial images. The prominent areas where wrinkles are developed naturally in human as age increases are taken for feature extraction. Further these feature vectors are subjected to ensemble techniques that increases the accuracy of the model hence improving the efficiency in terms of MAE and performance parameters for age group classification. The proposed approach was evaluated on FG-NET facial aging dataset.

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Section: Introductionmentioning

confidence: 99%