## 1 Introduction  
Effective management of pain, including assessment and tracking overtime, is necessary to study the effectiveness of different treatments [1, 2] and to avoid reaching chronic pain syndromes [2]. Pain can be assessed either by patients' self-reporting or by medical member observations [1, 3]. The self-report method on a verbal or visual scale is the gold standard of pain assessment [1, 4], but this method can be inaccurate and challenging in people with severely impaired communication ability (e.g., people with later stage dementia [5], burn-injured adults [1], neonates [6], intensive care patients [7], etc.). There are two common methods for pain assessment based on the observation principle: the Critical Care Pain Observation Tool (CPOT) and the Pain Assessment in Advanced Dementia Scale (PAINAD) [1]. The CPOT and PAINAD tools are designed for patients unable to self-report [1]. The CPOT assesses facial expressions, body movements, ventilator compliance, and muscle tension/rigidity, while the PAINAD assesses breathing, vocalization, facial expressions, body language, and consolability [1]. Facial expressions form the basic components of pain assessment in both the CPOT and PAINAD tools. Studies have shown that periodically monitoring patient pain level in intensive care units in hospitals improved patient outcomes [8]. However, the medical member observations methods are highly subjective; human observers may be influenced by personal factors [6] and observers need to have the required experience [9]. Further, contentious pain assessment monitoring may fail and not be sustained when there is a shortage of medical experts [9]. These causes show the need for an automatic system to monitor a patient's pain level [6; 8; 9], which can also be used at home for elderly people [9]. A previous study that compared the performance of a human observer's pain assessment with that of an automated pain assessment from facial expressions concluded that the latter outperforms the former and is more reliable [6]. However, the tasks of pain detection and assessment from the images is challenging due to the diversity in head poses and environments, such as the illumination conditions and intensive occlusion [8; 9]. Furthermore, patients tend to show a high variance in their reactions and facial expressions to the same level of pain [8]. The actions that form the facial expression of feelings, such as lowering the eyebrows or squinting, are called action units (_AU_) and each is assigned a unique code (e.g., _AU6_ is lifting the cheeks). Regarding pain expression, six _AU_'s (_AU4_, 6, 7, 9, 10, and 43) have been found to be the most representative [10]. By assigning numerical values to those six AUs, pain can be calculated via the Prkachin and [PERSON] pain intensity (_PSPI_) equation, as shown below:  
\[PSPI\ pain=AU4+\max(AU6,AU7)+\max(AU9,AU10)+AU43 \tag{1}\]  
This _PSPI_ score determines pain intensity on a scale of 0 (no pain) to 16 (maximum pain). Below is a detailed explanation of the valuation of the six _AU_s used in _PSPI_:  
* _AU4_ is the intensity of lowering the eyebrows on a scale from 0 to 5 (0 = not lowered, 5 = maximally lowered). * _AU6_ is the intensity of raising the cheeks on a scale from 0 to 5 (0 = not raised, 5 = maximally raised). * _AU7_ is the intensity of tightening the eyelid on a scale from 0 to 5 (0 = not tight, 5 = very tight). * _AU9_ is the intensity of wrinkling the nose on a scale from 0 to 5 (0 = not wrinkled, 5 = very wrinkled). * _AU10_ is the intensity of rising the upper lip on a scale from 0 to 5 (0 = not raised, 5 = very raised). * _AU43_ is whether the eyes are closed; represented as a binary value (0 = opened, 1 = closed)  
In this paper, we developed a new facial-expression-based automatic pain assessment system (FEAPAS). The model uses a dual convolutional neural network classifier to detect pain from facial expressions. The dual model better imitates the human brain's visual perception [11].