###### Abstract
While both the inter-rater reliability and the test-retest reliability of human emotion analysis within the FACS framework have been explored, in the case of FACS-based AE-FEA systems, perfect test-retest reliability was tacitly taken for granted (see below) and, instead, inter-system reliability studies were published. In most of these, the systems' performances were evaluated individually and sequentially against reference datasets, and then compared. In other studies, agreement between the different systems was directly assessed. Study outcomes are not easy to compare, because diverse kits of stimulus datasets and statistics were used. This is true of human-computer inter-system comparisons [3; 5; 10; 34; 35; 38; 39]. A smaller number of inter-system comparison studies have employed more sophisticated methods, where aspects of emotion dynamics, such as onsets, peaks, and offsets of emotions, or changes in action unit intensities, were also compared [1; 20]. Overall, the results indicate that AEFEA performs almost equally to, or occasionally better than, the reference human coders. An important exception is the study by [PERSON] et al. [21], where human coders outperformed all eight of the AEFEA systems, including FaceReader (version 7), in analyzing both posed and spontaneous EFs. Electromyography (EMG)/AEFEA comparison studies found that the two types of systems yielded highly correlated output data; however, compared with EMG, AEFEA systems are much easier to use [25; 38; 40]. In the case of human rating, test-retest reliability is perceived as an important methodological issue concerning EFEs as well as other aspects of human behavior. However, we found no study exploring the test-retest reliability of any AEFEA system. Most research reports seem to assume, either explicitly or implicitly, that AEFEA systems are based on fully deterministic algorithms, thus taking their perfect test-retest reliability for granted and implying that the stability of their input-output mappings is not an interesting question for research [41]. To fill this gap in our knowledge on the characteristics of AEFEA systems, the present study aimed to explore the test-retest reliability of a specific AEFEA engine, FaceReader 8.0 (henceforth referred to as FR8). We explored the test-retest reliability of FR8 in a group of subjects with atypical neurocognitive development, namely children with ASD, in addition to a sample of neurotypical children. The quality and quantity of EFEs have diagnostic significance in several conditions, including ASD [9; 42]. This makes AEFEA technology an important candidate research tool in studying these groups and in developing technology-aided screening and diagnostic systems. In studying EFEs in atypical populations (including both individuals with developmental disorders and those with other psychiatric conditions), three aspects of emotional display seem especially relevant. These may potentially influence the validity and/or reliability of EFE recognition methodologies (both human and automated ratings) in these groups. The three aspects are as follows: (1) the congruence of facial emotions, i.e., the adequacy of an EFE in a specific context [9; 43]; (2) the match between the emotion subjectively experienced and the corresponding facial expression [44]; (3) facial morphology [45; 46]. Autism spectrum disorders are neurocognitive developmental conditions, and atypical communication of emotions is among their diagnostic markers [47]. Although findings on EFEs in ASD are diverse, studies suggest that all three anomalies mentioned are present in ASD: atypical craniofacial features and development [48; 49; 50; 51; 52]; incongruent emotions and mismatch between the expressed and experienced emotions [53; 54; 55; 56]. It was also found that some individuals with ASD tend to use EFEs intentionally instead of spontaneously in social contexts, in order to \"camouflage\" their condition [57].