Face alignment is one of the most active fields of computer vision. It attempts to localize facial semantic landmarks from a given face image

which is an important step in many face-related vision tasks such as face recognition and face beautification. Cascade regression-based face alignment algorithms have recently achieved state-of-the-art performance in both accuracy and speed. Cascade regression is an iterative method that refines an initial face shape through many linearly combined weak regressors. However

most previous methods focused on boosting the learning method or extracting geometric invariant features while ignoring the initial shape quality. This approach severely lowers their accuracy on complex scenarios

such as exaggerated expressions or extreme head poses. This study proposes a cascade regression-based multi-pose face alignment algorithm initialized with estimated initial shapes. The proposed method consists of two parts. First

the first derivative of Gaussian filter-based gradient difference features is extracted to represent the facial appearance

and a random regression forest is learned to predict initial face shapes. Second

these initial shapes are regressed by particular cascade regressors separately. The alignment error of this method decreased by 29.2%

13.3%

and 9.2% in the COFW

HELEN

and 300 W databases

respectively

unlike existing methods. Experiments show that this method can eliminate the disturbance among different shapes for more accurate multi-view face alignment and run in real-time. This study proposes an algorithm for multi-pose face alignment based on cascade regression. This algorithm surpasses many state-of-the-art methods in terms of accuracy and is more robust for complex face shapes. The proposed initial shape estimation algorithm can generate initial shapes in suitable quality applied to improve existing cascade regression-based methods.