Compound Stratum Practice

A person’s face can reveal a lot of information about them, including their age, gender, and identity. Faces play a crucial part in estimating and predicting a person’s age and gender simply by looking at them. Computer vision and psychophysics researchers encounter issues such as perceiving human faces and modelling the specific aspects of human faces that contribute most to face recognition. Many strategies for age and gender classification based on face features have been proposed in the literature.

In this book, feed forward propagation Neural Networks at the coarser level are used to categorise human age and gender. In the Compound level, the final classification is done utilising 3-sigma control limits. The proposed method effectively distinguishes three age groups: children, middle-aged adults, and senior citizens. Similarly, the proposed Compound Stratum technique defined two gender groupings as Male and Female.

Compound Stratum Practice with 3 Sigma Control Limits applied to the output of the Artificial Neural Network classifier improves the system’s performance even further. On the output given by the Neural Network classifier, the Mean and Standard Deviation were considered, and 3 sigma control limits were used to set the range of values for the specific category of age and gender. The system’s efficiency is proved by the experimental results obtained utilising benchmark database images.

Author(S) Details

M. R. Dileep
Department of Master of Computer Applications, Nitte Meenakshi Institute of Technology, Bengaluru, Karnataka, India.

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