Introcudtion
Humans have long sought to decode personality from the face. In the Arabic world this tradition is known as al‑Firāsa (face‑reading), a cultural practice that stretches back centuries. Faraseh builds on that heritage with modern psychology and artificial‑intelligence research to translate a single selfie into a reliable personality snapshot.
Pillars of Our Analysis
- Facial Action Coding System (FACS): We break each selfie into anatomical “Action Units,” the micro-movements catalogued by Ekman & Friesen. Automated FACS lets our software quantify even the tiniest muscle contractions.
- Big Five Personality Model: We map facial cues to the most widely replicated personality framework in psychology: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism (OCEAN).
- Al-Firāsa (Arabic Physiognomy): Classical insights serve as qualitative priors. Historical texts linked observable facial patterns with character; Faraseh validates and refines these associations with data.
- Machine Learning & AI: Convolutional and boosting models learn the complex mapping from hundreds of facial key-points to Big Five scores. Peer-reviewed studies like Kachur et al. 2020 confirm the viability of this approach (average r ≈ 0.24–0.36).
- Large-Scale Data: Our training corpus exceeds 120 000 user-consented faces plus public research datasets. This scale enables robust accuracy (mean r ≈ 0.30–0.40 with self-reports) and high test–retest reliability (> 0.8).
Facial Expression and FACSThe Facial Action Coding System objectively codes every visible muscle movement—such as an inner-brow raise (AU 1) or lip-corner pull (AU 12). Our engine detects these Action Units in milliseconds, turning subtle emotional cues into quantitative data. Research shows that habitual expression patterns correlate with personality traits; for example, frequent genuine smiles often align with higher Extraversion.
Machine Learning and Big Five MappingHundreds of facial key-points and FACS scores feed a neural network that outputs percentile scores for each Big Five trait. In validation tests over 5 000 users, predicted traits correlate 0.30–0.40 with questionnaire responses—comparable to benchmarks reported in leading journals. Conscientiousness and Extraversion typically show the strongest signals.
Reliability and ValidationWe benchmark our model using split-half, test–retest, and bias-auditing protocols. Internal studies show personality scores remain stable (±3 points) across two selfies taken a week apart, and fairness checks reveal < 2-point mean absolute error across gender and Fitzpatrick skin-tone groups.
Privacy and EthicsSelfies are processed locally; only anonymised embeddings reach our cloud and are deleted after 30 days unless users opt in to research. A human-in-the-loop system reviews low-confidence cases, and differential-privacy fine-tuning protects user identities during continual learning.
Key Scientific Sources- Ekman, P. & Friesen, W.V. (1978). Facial Action Coding System: A Technique for the Measurement of Facial Movement.
- Kachur, V. et al. (2020). Predicting the Big-Five Personality Traits from Facial Static Images.
- Cai, Q. & Liu, Y. (2022). Personality Recognition from Facial Dynamics with Gradient Boosting.
- PerfEx Dataset (2024). Large-scale Action-Unit and Personality Annotations for Video-based Analysis.
- Classical Arabic Treatises on Al-Firāsa (various authors, 8th–14th centuries).