# Emotion Detection Using Facial Expression Involving Occlusions and Tilt
###### Abstract
The second method rather chooses the partitioning of the face image into sub-segments and then requires the processing of those sub-segments. The techniques required to detect the expression of a person broadly follow a three-step process: face tracking, feature extraction, and classification. Face detection is a process in which a face is located in a frame. Identifying a face in a frame is a procedure known as face detection and is viewed as the preprocessing step in emotion detection [5;6]. The ability of computers to recognize human action is one of the most important applications of computer vision. It can be used for a variety of things, such as monitoring children and the elderly, creating sophisticated surveillance systems, and facilitating human-computer interaction. The process that comes next is feature extraction after the face has been detected. It is employed to gather the face's main feature points, which serve as a representation of such features. The main goal of feature extraction is to convert the important aspects of the data into numerical characteristics that can then be employed in the machine-learning process. The final phase is classifying photos into informational categories. The process of classification uses a decision rule to partition the space of spectral or spatial features into different classes. There are four main techniques to detect the face in a single image: knowledge, feature, template, and appearance-based methods. However, some hybrid techniques are also used for emotion detection. A knowledge-based method is a top-down approach. In this method, the face is located with the help of human-coded rules, such as features of the face, skin color, and template matching. These basic rules are very easy to implement, for example, two eyes are symmetric to each other a nose and a mouth [6]. Skin color is unique because it does not change with a change in position or occlusion. However, skin color varies from person to person and with regions. The main problem with this method is to convert human-knowledge-based rules into codes. If the rules are too strict, the face will not be detected; if the rules are too general, the rate of false detection will increase. The other problem with this approach is that it cannot detect a face in different positions or poses [7]. The feature-based method is a bottom-up approach [6] and works to find basic facial features to locate faces in various poses, viewpoints, or light. It is designed for face localization. The feature-based method is subdivided into four kinds: facial feature, texture feature, skin color feature, and multiple feature-based methods. The problems with this method are illumination, noise, and occlusion, which cause the corruption of features that makes it harder to detect edges of features or detect many edges, which makes the algorithm inoperable [6]. If only because template-based approaches are simple to use, they do not capture overall facial structure. To distinguish between a group of five emotion expressions (entertainment, rage, contempt, right, and sorrow) in movies from the BioVid Emo database, the face in videos is detected, and spatial and temporal characteristics (points of interest) are extracted [8]. In the appearance-based method, templates are prepared from a number of training images that capture the various forms of facial appearance. In contrast to the template-based method in which the template is designed by experts, in the appearance-based method, a learning approach is adopted to analyze the image to make a template. These templates are the models for face detection. Multiple techniques and analyses are performed to find different characteristics of images. These procedures are designed primarily for the detection of the face, which determines face and non-face frames [6]. The most popular face detection algorithm now is the Viola-Jones method.