Abstract
Automatic verification and identification of face from facial image to obtain good accuracy with huge dataset of training and testing to using face attributes from images is still challengeable. Hence proposing efficient and accurate facial image identification and classification based of facial attributes is important task. The prediction from human face image is much complex. The proposed research work for automatic gender, age and race classification is based on facial features and Convolutional Neural Network (CNN). The proposed study uses the physical appearance of human face to predict age, gender and race. The proposed methodology consists of three sub systems, Gender, Ageing and Race. Therefore different feature are extracted for every sub system. These features are extracted by using Primary, Secondary features, Face Angle, Wrinkle Analysis, LBP and WLD. The accuracy of classification is based on these features. CNN used to classify by using these features. The proposed study has been evaluated and tested on large database MORPH II and UTKF. The performance of proposed system is compared with state of art techniques.
Highlights
The face of human holds significant amount of qualities and data about the human identification, for example, appearance, race, gender classification, and age
The planned work is organized for race, age and gender estimation conjointly, during this study multilayer design developed for age, gender and race classification supported by Convolutional Neural Network (CNN)
The planned work is organized for age, gender and race estimation conjointly, during this study multilayer design developed for age, gender and race classification supported Convolutional Neural Network (CNN)
Summary
The face of human holds significant amount of qualities and data about the human identification, for example, appearance, race, gender classification, and age. Humans can identify and examine these data effectively, for example, most of human can perceive human attributes like gender classification [1], and they can judge if the human is male or female by just observing face. They can estimate the age of the individual and state whether this individual is a kid or a grown-up. Age predicting gender classification and race forecast frameworks has been developing quickly as of late due its significant modules and valuable uses for some machine vision applications in seeing a particular portion of individuals. In [4] For instance, clothing stores may offer suitable styles for guys or females as per their age gatherings; cafés required to know the mass prominent suppers for each age or gender classification gathering, multiple organizations need to make explicit publicizing to explicit spectators relying upon their gender, age and race estimation structures [4]
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