Under Constrained Conditions a Structure-Based Human Facial Age Estimation Framework PROJECT TITLE : A Structure-Based Human Facial Age Estimation Framework under a Constrained Condition ABSTRACT: In computer vision and pattern recognition, developing an automatic age estimation approach for human faces continues to play a significant role. Many works on face age estimate concentrate on two aspects: extracting facial aging features and learning classification/regression models. To distinguish our work from existing age estimation approaches, we investigate a novel aspect-system structure that is constrained: how to create a framework to increase age estimation performance based on the constraint given a fixed feature type and a fixed learning method? For facial age estimate, we present a four-stage fusion architecture. This framework begins with gender identification, then moves on to the second phase, gender-specific age grouping, the third stage, age estimation within age groups, and finally the fusion stage. Three well-known benchmark datasets, MORPH-II, FG.Net, and CLAP2016, are used in the experiment to validate the approach. The experimental results show that adopting our suggested framework improves performance greatly, and that it also beats various state-of-the-art age estimation methods. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Support Vector Machines, Feature Extraction Python Artificial Intelligence Projects Python Deep Learning Projects Python Image Processing Projects Task Analysis Neural Networks An economic employment approach to an analytical model of E-recruiting investment decision A Probabilistic Method for Detecting Fires in Videos Based on Vision