Extracting facial data has recently drawn more attention due to an ongoing progress in research and increase in its accuracy. Thanks to mobile platforms, the necessary but ordinary cameras are remarkably widespread nowadays. Big companies, like Google, have entered the market with their own cloud-based and mobile face analysis solutions. Possible applications for extracted facial data range from more user-friendly devices to user-interface evaluation and marketing. This thesis starts with an introduction to the topic of facial data extraction. It outlines several interesting applications, important techniques and existing frameworks first. Afterwards, the main contribution is presented: OpenFace++, an improved and extended variant of the existing OpenFace face analysis framework. Like OpenFace, the proposed OpenFace++ framework aims to close an unsatisfying gap in available functionality between commercial and free open-source solutions. OpenFace++ adds several new features to OpenFace. Amongst others, the added highlights are facial expression recognition, attention estimation, and cross-platform compatibility. OpenFace++ works on Android out-of-the-box. The work is completed by presenting a user-study which was conducted to investigate the reliability of extracted facial data. The first experiment evaluates the detectability of eye-closeness states. Results show that facial data extraction can outperform competing solutions. The second experiment shows that face orientation extraction also works reliably compared to a gyrometer. Finally, the third experiment demonstrates that the new facial expression detection can detect many cases very accurately. The thesis finishes with some suggestions for further improvements on OpenFace++ in the future.