Abstract
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.
Highlights
In the age of information processing the systems have reduced the complexity of Human – Machine interaction
The local Binary Pattern (LBP) features for the individual cropped images comprise of 59 feature vector values that are combined together with other feature values, these values are normalized, standardized and resampled to perform the ensemble techniques to improve the accuracy of the proposed model for age classification
The proposed approach using ensemble technique for improving the accuracy of the model is applied to the feature set comprising facial texture
Summary
In the age of information processing the systems have reduced the complexity of Human – Machine interaction. The proposed method uses relative order information for rank prediction amid age labels In this approach, the age range is achieved by accumulating a series of binary classification outcomes, in which cost sensitivities are introduced within labels to improve aggregate performance efficient descriptor, a dispersing transform that disperses the Gabor coefficients and combines Gaussian smoothing in multiple layers, is assessed for the extraction of facial features. The LBP features for the individual cropped images comprise of 59 feature vector values that are combined together with other feature values, these values are normalized, standardized and resampled to perform the ensemble techniques to improve the accuracy of the proposed model for age classification. The details of extracted features that are cropped from the given facial image is given in Table-1
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