Abstract: Automatic facial features localization is capable of affording accurate face location which is highly desirable for face analysis. Active shape model (ASM) and active appearance model (AAM) are efficient frameworks for facial features localization. However, the performance of these conventional methods is unsatisfactory under pose, illumination and facial expression changes. To overcome these drawbacks, an automatic and accurate facial features localization algorithm is proposed which has two improvements: 1) utilizing efficient machine learning methods (random forest classifier and pair-compared feature) to construct local appearance model to deal with illumination and expression changes; 2) incorporating classifier outputs and shape constraint into the quadratic optimization. Experimental results over many images with obvious pose, expression and illumination changes have shown the accuracy and efficiency of our method.