# Emotion Detection Using Facial Expression Involving Occlusions and Tilt
## 2 Related Work
Recent academic research on emotion recognition typically uses convolution neural networks (CNN) [17; 18]. CNN has proved to be a promising application for face detection, feature extraction, and classification. This method automatically extracts a characteristic and classifies it, eliminating the need for handmade methods. Convolution layers, activation function, subsampling, and dense layer are the four fundamental components of CNN (fully connected layer). However, several occlusion-based instances of perplexed face pictures were incorrectly identified by a CNN model based on pre-trained deep learning. In [19], the authors used the CNN model to obtain features from depth information. The model is based on two layers: The feature map at the first layer is 6 and kernel size is 5, then a max pooling is used. The second layer is based on 6 feature maps and a kernel size is 5, max pooling is 2, and then 12 feature maps, and finally Softmax is used. The proposed approach is an illumination variant and obtains an 87.98% accuracy with 1000 epochs. The authors present a fusion of two models for emotion recognition in [20]. The multi-signal convolutional model (MSCNN) is used to get spatial features statically and the part-based hierarchical recurrent neural network (PHRNN) is used to get temporal features dynamically and combine them. The PHRNN model is a 12-layer model whereas the MSCNN model has 6 layers. The study [21] presents a FER model based on CNN which has 3 convolutional layers and consists of 5 \(\times\) 5 filter size. The authors used the dropout layer as the regularization layer. The proposed model obtains an emotion recognition accuracy of 96% in 3 min. In [22], two convolutional layers are used; first with 5 filter sizes and the second one with 7 filter sizes. The max-pooling layer has a 2 \(\times\) 2 kernel to reduce the size while the dense layer has 256 hidden neurons. Its learning rate is 0.01 and the training was performed using 2000 epochs. The study obtained promising results, yet ignored the occlusions and illumination variations. The CNN architectural paradigm, which employs the FER2013 database for emotion recognition, is suggested in the paper [23]. The dataset includes 32,298 90 \(\times\) 50 pixel photos. To enhance the performance and to generalize the training and dropout, the authors used regularization techniques. It uses a batch size of 128 after each dense layer. Using 40 training epochs, an accuracy of 74% was attained. Table 1 presents a comparative review of the discussed research works. It describes the process used to detect the face, preprocessing involved in the approach, the feature extraction approach, the classifiers used for emotion classification, and the reported accuracy. The most common classifier used for emotion detection are decision tree [13; 14; 15], SVM [24; 25; 26; 27; 28] and neural networks [23; 30]. SVM is very effective in terms of memory management and dimensionality. On the other hand, the performance is affected because larger datasets need a longer time in the training phase, and data have more noise. SVM also does not directly provide probability estimates, and these have to be computed separately. The objective of this review is to view the trends in composition studies within the past years and see how emotions are detected using facial expressions. It is clear from the research that mainly six to seven basic emotions are detected. Predominantly, the Viola-Jones method is adopted for face detection in a frame, and then landmarks or LBP descriptors are used for feature extraction. PCA is applied for dimensionality reduction, and SVM is used for emotion classification. The average accuracy gained by the researcher (15 different methods) is 81.77%. It was observed that the RBF error reduction method is the most efficient. Most of the work was performed in feature extraction, but research work is moving more toward CNN, as it is more efficient and does not need hand-crafted methods to improve performance.