Geometric-Based Facial Feature Localization Method

Abstract; This paper is a comment on geometric based facial feature localization method proposed by Dehshibi and Bastanfard, called combined projection function (CPF). INTRODUCTION In this paper, we deal with the problem of facial feature position identification by linear principal transformation (LPT). Facial feature extraction generally refers to the detection of eyes, mouth, nose and other important facial components. This is the most important preparatory stage for developing applications such as face recognition [], facial expression [], face detection [], age classification [], gender classification [].

Based on the eigenspace, we proposed a vectorized set of three features including principal component analysis (PCA) including WF, FFP and RFF. Positioning of facial feature points is obtained by a new algorithm. For this purpose we will review briefly some of the previous methods in this section. - Two important articles exploring how young people interact with global culture from different perspectives are the UN's "Young People in the Globalized World" (2003) and Arnett's "Psychology of Globalization "(2002) It is. In the article, the United Nations uses socio-economic methods to study how young people are involved in world cultures. They argue that the participation of young people is different depending on their financial resources and location.

In the geometric feature method, the features and shapes of different facial features are combined to form a feature vector representing a face, whereas in an appearance-based system, an image filter Is applied. The expression changes. . The geometric feature method requires reliable facial features, which are often habitual obstacles. On the other hand, the performance of the appearance-based approach decreases with changes in the environment. The proposed LDN method recognizes facial expressions robustly under various changes such as sadness, anger, happiness, and aversion.

Dynamic facial analysis based method, including head pose estimation from video and facial landmark positioning. Compared to traditional Bayesian filters, the RNN-based approach learns joint estimates of frame-by-frame measurements and tracking them over time in a single end-to-end network. In addition, it does not depend on the complex and problem specific tracker engineering or feature engineering required in existing methods. This makes the RNN-based approach a general-purpose method that can be extended to other tasks in video face analysis, besides being shown in this article.