## 1 Introduction
Thus, an automatic discomfort detection system for infants by analyzing their facial expressions in videos from a dataset collected at the hospital Maxima Medical Center in Veldhoven, The Netherlands, was presented in [16]. The authors used again HOG, LBP and SVM with 83.1% correctly detected discomfort expressions. Finally, [PERSON] et al. [14] used CNNs with transfer learning as a pain expression detector, achieving 90.34% accuracy in a dataset recorded at Tampa General Hospital and in [15] obtained an accuracy of 91% for the NPAD database. On the other hand, concerning emotion recognition and wearable devices, most of the proposed methods until now relied on biomedical signals [17; 18; 19; 20]. When using images, and more specifically, facial images to recognize emotions, very few wearable devices can be found. Among them, one can find the work by [PERSON] et al. [21], where they proposed a glasses-type wearable system to detect a user's emotion using facial expression and physiological responses, reaching around 70% in the subject-independent case and 98% in the subject-dependent one. In [22], another system to automate facial expression recognition that runs on wearable glasses is proposed, reaching a 97% classification accuracy for eight different emotions. More recently, [PERSON] and [PERSON] described in [23] another glassed-type wearable device to detect emotions from a human face via multi-channel facial responses, obtaining an accuracy of 78% at classifying emotions. Wearable devices have also been designed for infants to monitor vital signs [24], body temperature [25], health using an electrocardiogram (ECG) sensor [26], or as a pediatric rehabilitation device [27]. In addition, there is a growing number of mobile applications for infants, such as SmartCED [28], which is an Android application for epilepsy diagnosis, or commercial devices with a smartphone application for parents [29] (smart socks [30] or the popular video monitors [31]). However, no smartphone application or wearable device related to pain detection through facial expression recognition in infants has been found. Therefore, this work investigates different methods to implement a reliable tool to assist in the automatic detection of pain in infants using computer vision and supervised learning, extending our previous work presented in [2]. As mentioned before, texture descriptors and, specifically, Local Binary Patterns, are among the most popular algorithms to extract features for facial emotions recognition. Thus, this work will compare the results after applying several texture descriptors, including Radon Barcodes, which is the first time that they are used to detect facial emotions, this being the main contribution of this paper. Moreover, our tool can be easily implemented in a wireless and wearable system, so it could have many potential applications, such as alerting parents or medical staff quickly and efficiently when a baby is in pain. This paper is organized as follows: Section 2 explains the main features about the methods used in our research and outlines the proposed method; Section 3 describes the experimental setup and the set of experiments completed and their interpretation; and, finally, conclusions and some future works are discussed in Section 4.