Generalized Multi_stage Feature for Deep Age Estimation from a Human Face Image

Although there is a lot of research work in the image processing field, The digital world has a special focus on the field of humain face estimation. Given the variety of challenges faced, various aspects must be considered when estimating age. Estimate human age can be cultivated by beholding geometric dimensions and textural analysis. Against this background, in this task, present an operational performance comparison accurate age estimation of faces using various regression SVR and MLR methods on RGB images. The next steps are face detection, cropping and resizing, dimensionality reduction using W_PCA and PCA methods, feature extraction using singular scale HOG method, and finally robust regression To offer a sophisticated estimation of the age of the facial image. Furthermore, we evaluate the performance of the learned model in the presence of face images. The value and efficiency of this design can be demonstrated through extensive experiments large internal age databases as well as the publicly available MORPH II and PAL databases.