Our approach to facial analysis for personality prediction leverages supervised learning. We combine machine learning and deep learning techniques, training our models on tens of thousands of labeled images. These images establish correlations between facial landmarks and personality traits. Our state-of-the-art algorithms identify patterns within this data that link to specific personality types.
To ensure reliable predictions, we utilize a robust dataset. Over a decade of expertise in personality profiling has allowed us to curate a collection of over 40,000 facial images. This diverse dataset includes individuals of various genders, races, and geographical backgrounds. Importantly, to mitigate bias, all images are anonymized before processing with our algorithms.
The science behind our approach is well-supported by academic research. Numerous peer-reviewed studies published in respected journals like Nature, PubMed, IEEE, PLoS One, MDPI, and ScienceDirect demonstrate the link between facial features and personality traits in images (automated personality prediction).
Our AI system goes beyond basic analysis. We employ an ensemble model, combining more than three different algorithms to arrive at the most likely personality traits. Some of these advanced algorithms perform 3D face reconstruction for additional data points. Additionally, facial recognition algorithms generate face embeddings for comparisons, while Facial Action Units (FACS) allow for in-depth analysis of facial expressions.