Quantitative analysis of facial soft tissue using weighted cascade regression model applicable for facial plastic surgery

Localization of facial landmarks plays an important role in the measurement of facial metrics applicable for beauty analysis and facial plastic surgery. The first step in detecting facial landmarks is to estimate the face bounding box. Clinical images of patients' faces usually show intensity non-uniformity. These conditions cause common face detection algorithms do not perform well in face detection under varying illumination. To solve this problem, a modified fuzzy c-means (MFCM) algorithm is used under varying illumination modeling. The cascade regression method (CRM) has an appropriate performance in face alignment. This algorithm has two main drawbacks. (1) In the training phase, increasing the real data without considering normal data can lead to over-fitting. To solve this problem, a weighted CRM (WCRM) is presented. (2) In the test phase, using a mean shape causes the initial shape to be either near to or far from the face shape. To overcome this problem, a Procrustes-based analysis is presented. One of the most important steps in facial landmark localization is feature extraction. In this study, to increase detection accuracy of the cephalometric landmarks, local phase quantization (LPQ) is used for feature extraction in all three channels of RGB color space. Finally, the proposed algorithm is used to measure facial anthropometric metrics. Experimental results show that the proposed algorithm has a better performance in facial landmark localization than other compared algorithms.