A review and Comparing different of the most commonly Criteria for human age estimation techniques

From a single human face image, we can collect a wide variety of information, including age, identity, gender and ethnicity. In recent years, age estimation from face image has gained a considerable attention in wide range of applications, various approaches, databases, and evaluation protocols have been proposed.In this paper, the aim of our work is to determine the accuracy of Principal Component Analysis (PCA) method regardless of the influence of external factors after features extraction, and, second, to compare age estimation with and without using dimensionality reduction. We also propose to use Multi-Block Local Binary Pattern (MB-LBP) rather than using Local Phase Quantization (LPQ) and Binarized Statistical Image Features (BSIF) descriptors. To estimate the age of different human face image, we use Support Vector machine Regression (SVR). Our results comparing different of the most commonly Criteria namely Mean Absolute Error (MAE) and Cumulative Score (CS). We prove our methods on FG-NET database and achieve state-of-the-art results for age estimation.