# Emotion Detection Using Facial Expression Involving Occlusions and Tilt
## 2 Related Work  
Prior research placed a strong emphasis on the projection of facial expression, highlighting and identifying the most prevalent emotional traits. However, as time went on, the idea of human-computer interaction and artificial intelligence increased the importance of emotion recognition. Researchers suggested employing local binary pattern histogram and Haar-like features with a cascade classifier to recognize a person's face in real-time movies [9], but no significant work has been conducted to determine emotions. Vertical projection is applicable to discover the limits of the lips before horizontal projection is used to locate the mouth on the identified area of the face. The Viola-Jones algorithm is used for face detection in a variety of settings, including camera distance,backdrop color, object orientation, etc. So, in [10; 11], multi-level systems are proposed that include algorithms such as feature extraction, feature reduction, and principal face detection using the Viola-Jones algorithm. The region of interest (R.O.I), or feature portion of the image, is determined or removed via feature extraction. Despite the fact that this stage is the most crucial and significant one, enough technical information was overlooked. The choosing procedure in this stage determines the efficiency of the system [12]. There are a large number of combinations used for feature extraction and classification. Feature extraction can be differentiated into two groups: learned and pre-designed [12]. Pre-designed feature extraction is handcrafted however learned is an automatic way of feature extraction. Pre-designed features are further divided into two main groups: appearance-based features and geometric features. Additionally, a combination of both of them called the hybrid technique is frequently used [13; 14; 15]. The most common facial feature extraction techniques are principal, local binary pattern (LBP), Gabor features, and principal component analysis (PCA). However, PCA is mostly used for dimensionality reduction. Landmark and facial points are used for face localization and are used alone or combined with Gabor, LBP, or histogram of oriented gradients (HOG) to extract more accurate features [13]. Classification is the final phase of expression analysis; computational methods are used to improve performance, for instance, to make accurate predictions. The expression can be classified directly or first recognizing certain action units. The study [14] employs a support vector machine (SVM) in an e-learning system to identify emotions. The achieved accuracy varies from 89% to 100% with respect to the dataset used for testing. To examine the classifier performance, test samples are used [15]. During the training phase, the machine learning algorithm creates a model of the input and creates a hypothesis function for data prediction in [15]. One way that machines might recognize facial expressions is by examining the changes in the face when the expressions are shown. The optical flow technique is used to obtain the distortion or vibration vectors caused by facial expressions in the face. The analysis is then performed using the vibration vectors that were gathered. They are employed to benefit from their positions and orientations for automatic facial expression recognition using a variety of data-mining techniques. During the training phase, the machine-learning algorithm builds a model of the input and creates a hypothesis function for data prediction. Ref. [14] presents a robust approach for facial expression classification using pyramid HOG and LBP features. Hybrid features are extracted from patches of the face that undergo major change during a change in expression. Experimental results using SVM indicate a 94.63% expression recognition rate using the CK+ dataset. The robustness and accuracy of recognizing female expressions are improved by SVM-based active learning in [16] at a higher pace than male emotions. Surprise and fear, on the other hand, have lower rates of emotion recognition.