# Deep-Learning-Based Models for Pain Recognition: A Systematic Review
## 3 Review Papers
### Single-Model-Based Pain Recognition
#### 3.1.3 Facial Expressions  
Facial expressions are signals that have received attention from researchers for many applications, such as face recognition in the field of biometrics. Deep learning is used directly to estimate the pain from face expressions. One of the distinct approaches is to estimate the pain from the self-reported visual analog scale (VAS) pain levels to understand the individual differences [6]. Their method includes two learning stages. The first one, which is performed by learning the recurrent NNs (RNNs), estimates the Prkachin and Solomon pain intensity (PSPI) levels from face images. Then, personalized hidden conditional random fields (HCRFs) used the previous output to estimate the VAS for each person. By making comparisons with non-personalized approaches, this approach achieved high performance, and the score for a single-sequence test is the best. Deep learning has been mainly used to extract the important features, as was recently done by [7] for pain detection based on facial expressions. Their approach is based on three steps: First, convolutional neural networks (CNNs) are used to extract the features from VGG_Faces. After that, the result of the feature map is used to train the LSTM. This is a type of RNN used to find the binary pain estimation (pain, no pain). They provided a summary of previous works done on the popular data set of pain detection using faces. This data set is called a UNBC-McMaster database and has 200 video sequences from 25 patients who suffered from shoulder pain. Their experiments on this data set showed that their approach outperforms all previous works with an area under the curve (AUC) performance of 93.3%. Their model can also be generalized for application to other facial emotion recognition. Thisability was realized when applying their model to the Cohn Kanade + facial expression database and resulted in a competitive score (AUC = 97.2%). In the same manner, in 2017, [PERSON], [PERSON] and [PERSON] [8] proposed a pain-estimation model that combined learned features obtained from deep learning and other handcrafted features. Their idea comes from the hardness to obtain a data set for pain estimation in a large area in order to work well with deep learning. Therefore, they extracted handcrafted features directly from the face image and used a CNN to learn some features. Their features include appearance, shape and dynamics information. Finally, they classified the pain level using the linear regression model on the combined features and individual. Their results outperformed the state-of-the-art methods in terms of the root mean square error (RMSE) of 0.99 and Pearson correlation (CORR) of 0.67. A limitation of this approach--and all face-based approaches--is that they consider only the front of the face without capturing several combinations of indicators, such as audio, body movements and physiological signals. Another solution that aims to deal with small data sets and deep learning was proposed in 2017 by [PERSON] et al. [9]. They fine-tuned a small pain data set using a face verification network that is trained by the WebFace dataset, which has 500,000 face images. Then, they fitted a problem as a regression problem by applying a regression loss regularized with the center loss. Their performance was evaluated based on new proposed metrics to avoid the use of imbalance data. Based on the results, this method achieved a high performance compared with the state-of-art methods using both weighted metrics (mean absolute error (MAE): 0.389, (mean squared error) MSE: 0.804, (Pearson's correlation coefficient) PCC: 0.651) and new proposed metrics (weighted MAE 0.991, weighted MSE 1.720). However, pain is temporal and involves subjective information, and no such information and stimulus knowledge are used in this method, which requires further investigation. In contrast to previous solutions, the cumulative attribute (CA) method was used in 2018 [10] as a good solution to overcome the imbalance of data in pain estimation datasets.