###### Abstract
See [PERSON], [PERSON], [PERSON], and [PERSON]'s review [21] for others. Although all of these are based on FACS, they use various computational algorithms for face and feature (AU) detection [21]. There is intense simultaneous development in this area, which stretches beyond classical emotion theories and FACS. Analysis of facial expressions has been supplemented by the processing of a broader set of social signals [22]. Emotion identification has been extended beyond basic or primary emotions (fear, disgust, anger, surprise, happiness, sadness, and contempt, according to [PERSON]'s 1978 framework) to include secondary emotions (e.g., boredom, interest, and confusion) [23]. Facial expressions have also been decomposed into sequences of distinct phases, and their symmetry and micro-expressions, and the dynamic patterns of differences between morphologically similar facial expressions, have been analyzed [4]. Input data from laboratory settings are increasingly replaced with data from \"in-the-wild\" scenarios [2; 4; 22; 24]. In this study, we focus on FACS-based AEFEA systems, and, more specifically, on FaceReader 8.0. Although the number of AEFEA algorithms has increased in recent years, only a few studies on their validity and reliability have been published in peer-reviewed journals, e.g., [3; 52; 25]. More studies are available in conference presentations and proceedings [15; 26; 27; 28; 29]. Validation of AEFEA systems is conducted on controlled, publicly available reference datasets of images of human faces with EFEs and corresponding emotion intensities, as judged by trained human coders. Of the algorithms based on [PERSON]'s FACS, FaceReader, AFFDEX, and FACET have been best documented to reach high validity [3; 5; 20; 25; 30]. In regard to the FaceReader system specifically, three different versions of FaceReader have been validated on four different validated datasets of human expressions of basic emotions in total, three of which are publicly available. FaceReader 1.0 exhibited an 89% match with the reference emotion labels from the Karolinska Directed Emotional Faces (KDEF) dataset [20; 30; 31]. FaceReader 6.0 reached an 88% overall hit rate [5] when validated on the Warsaw Set of Emotional Facial Expression Pictures (WSEFEP) [32] and the Amsterdam Dynamic Facial Expression Set (ADFES) [33]. Human emotion recognition performance for these two datasets was 85% in the same study. For some action units, FaceReader did not reach the 0.7 level of agreement required for the FACS certificate for human coders; however, for other AUs, FaceReader showed high accuracy. It remarkably surpassed human coders in identifying neutral faces [34]. FaceReader 7 was validated [35] on the Standardized and Motivated Facial Expression of Emotion (SMoFEE) dataset [36], and its performance was compared with that of FaceReader 6.0 and of human coders found in earlier studies. Overall, FaceReader 7 performed well, as it correctly classified 79% and 80% of EFs (in noncalibrated and calibrated conditions, respectively); however, it showed somewhat uneven performance across emotions and underperformed FaceReader 6.0 in some respects. FaceReader is under continuous development; its most recent release is version 9. There have been considerable efforts to study both the inter-rater reliability and the test-retest reliability of human facial emotion analysis in the FACS framework. Inter-observer agreement was studied in various ways, such as using spontaneous vs. posed facial images and focusing on all AUs vs. specific AUs [1; 37]. The most-cited study on human-human inter-rater reliability within the FACS framework using spontaneous facial expressions in three independent laboratory studies is that of [PERSON] et al. [14], which reported good-excellent reliability measures.