In the past several decades, facial image analysis has attracted continuous attention in computer vision, pattern recognition and machine learning areas, owing to its scientific challenges in both psychological interpretation and computational simulation, as well as its huge potential in real-world applications. Much progress has been achieved in the last two decades; however, researchers in the field also meet bafflement and challenges on the comprehensive and unbiased evaluation of the related technologies, which may prevent them from discovering the actual state of the art. BeFIT - Benchmarking Facial Image Analysis Technologies- is an international collaborative effort on standardizing the evaluation of facial image analysis technologies. The objective is to bring together different face analysis evaluations and provide a medium for researchers to discuss about different aspects of face analysis. This interaction would also lead to new datasets or combination of existing datasets. The BeFIT webpage (URL: http://face.cs.kit.edu/befit) is planned to serve as a repository of facial image analysis technologies benchmarks and the regular workshops are intended to serve as a medium where the researchers can discuss about different aspects of face analysis. In this talk, the Benchmarking Facial Image Analysis Technologies -BeFIT initiative will be introduced and an overview of the proposed challenges, benchmarks, and the provided data sets within the BeFIT framework will be presented.