## 2 Background
### Reading Personality Attributes from Facial Characteristics  
Studying the relationship between facial and personality characteristics has a long history going back to antiquity. The book \"_Physiognomics_\", discussing the relationship between facial appearance and character, was written 300 BC in [PERSON]'s name, but is today attributed to a different author by most researchers. Swiss poet, writer, philosopher, physiognomist, and theologian [PERSON] published between 1775 and 1778 his magnum opus on physiognomy, \"_Physiognomice Fragmente zur Beforetung der Menschenkentntis und Menschenliebe_\" (Physiognomic fragments to promote knowledge of human nature and human love) [17], which cataloged leaders and ordinary men (therewere very few pictures of women) of his time by their facial shape, or what he called their \"lines of countenance\". [PERSON] even invented an apparatus for taking facial silhouettes to quickly capture the characteristics of a face, and thus the personality of the person. Later, statistician [PERSON] tried to define physiognomic characteristics of health, beauty, and criminology by creating composites through overlaying pictures of archetypical faces [18]. Italian criminologist and scientist [PERSON] continued this work by defining facial measures of degeneracy and insanity including facial angles, \"abnormalities\" in bone structure, and volumes of brain fluid [19]. For the better part of the 20 th century, scientists derogatively titled physiognomics as \"pseudoscience\". This changed towards the end of the 20 th century. While early physiognomists from [PERSON] to [PERSON] tried to develop manually assembled frameworks, AI and deep learning has given a huge boost to this emerging field. Recently, physiognomics has been experiencing renewed interest by researchers, particularly by comparing facial width to height ratio with personality. The theory of \"facial width to height ratio\" (fWHR) posits that men with higher \"facial width to height ratio\", that means with broader, rounder faces, are more aggressive, while men with thinner faces are more trustworthy [20; 21; 22; 23]. Recognizing these features automatically through facial emotion recognition has come a long way since the early days of the facial action coding system, thanks to recent advances in AI and deep learning. A large amount of research has addressed the issue of recognizing personality characteristics from facial attributes. For instance, ChaLearn \"Looking at People First Impression Challenge\" released a dataset with 10,000 15 s videos with faces ([[https://chalearnlap.cvc.uab.cat/dataset/20/description/](https://chalearnlap.cvc.uab.cat/dataset/20/description/)]([https://chalearnlap.cvc.uab.cat/dataset/20/description/](https://chalearnlap.cvc.uab.cat/dataset/20/description/)), accessed on 21 December 2021) [24], asking participants in the challenge to identify the FFI personality characteristics [8] of the person on the video, and their age, ethnicity, and gender attributes [25]. The problem with this dataset is that the personality attributes had been added by Amazon Mechanical Turkers, which sometimes leads to a biased ground truth, as it is based on guesswork by humans (the turkers). As was mentioned in the introduction, it has been shown by other researchers that accuracy of human labelers in recognizing emotions is only incrementally better than guesswork at slightly below 50 percent [2]. Nevertheless, the winners of the ChaLearn challenge have achieved impressive accuracy on this pre-labeled dataset to correctly predict the FFI personality characteristics at over 91% [26]. However, it would be better to have true ground truth on the personality characteristics of the subjects on the video. In another project using Facebook likes, where ground truth was available, the researchers showed that the computer was actually better in recognizing personality characteristics than work colleagues, who reached only 27% accuracy, while the computer achieved 56% accuracy [3]; spouses were the most accurate at 58%.