Attractiveness and Looks rating user statistics revealed
Last updated: December 14, 2025
Distribution of AI-Rated Attractiveness Scores (10,000 users)
Lookrank.com
The dashed line represents the theoretical General Population distribution (mean 5.0), while the pink bars show our actual user data.
This data represents 10,000 random users over a week from 01.12 - 07.12.
For public community users the AI scores were on average 0.3 points higher than human ratings. We're actively tweaking our algorithms to improve accuracy.
Data Privacy & Ethics
We never use data to train AI and we do not retain the images or geometric data of any of our users. We do, however, aggregate anonymous statistics of the scores our AI assigned to the last 10,000 users to provide these insights.
We Don't Sell Your Data
Our business is this tool. We're not a data broker. No trackers, no ad cookies, no creepy stuff.
Average Score
The exact weighted average of all users.
Most Common Score
The mode (most frequent score) with 287 users receiving this exact rating.
The 9+ Club
Only 239 users managed to get a 9 or above. No one achieved a perfect 10. The highest recorded score was 9.9 (only 2 users).
Top 10%
To reach the top 10% of all users, you would need a score of at least 8.2.
Why isn't the top 50% exactly 5?
- Self-Selection Bias: Most of our users are 18-25 years old and already interested in aesthetics, making our user base more attractive on average than the general population.
- Opportunity Cost: Extremely attractive individuals are statistically less likely to use the tool due to being busier on average and spending less time online.
- Demographics & Economy: The majority of our users are Western, correlating with higher disposable income. This gives better access to nutrition, skincare, and grooming standards that positively impact appearance.
Algorithm Accuracy
Error Margin
Think of your score as a consistent objective data point on how attractive you are, rather than a divine truth.
Mean Absolute Error (MAE)
On average, the AI's score differs from community-voted attractiveness by only 0.3 points.
Maximum Deviation
In the worst 10% of cases, AI scores deviate by an average of 0.96 points (typically scoring higher).
Bias & Objectivity Analysis
⚖️ Gender Bias
Surprisingly, we found no significant deviation when comparing Female and Male ratings by both the AI and Lookrank community users.
🤖 AI Objectivity vs 👥 Human Bias
This looks rating AI provides truly objective ratings based on aesthetic patterns, free from human cultural biases. However, we've noticed that human raters show bias depending on race, image quality, and background, which often leads to lower ratings.
For example, Southern Asians with good structural features can receive lower ratings from humans based on preferences that the AI hasn't been trained on. The AI evaluates facial harmony and bone structure without these cultural filters, while human perception is influenced by media exposure, familiarity, and unconscious preferences.
Challenges & Perception Gap
The "Harshness" Paradox
We often receive feedback that our AI is "too harsh," yet our data shows it actually rates users slightly higher than human voters on average. This discrepancy stems from the "Better-Than-Average Effect," where individuals consistently overestimate their own attractiveness.
Self-Rating Bias
Surveys show that both men and women most commonly rate themselves a 7 out of 10 [10]. When an objective AI scores them closer to the mathematical average (5-6), it feels like a penalty, even if the score is statistically accurate.
High Consensus
Despite individual taste differences, humans broadly agree on attractiveness. Scientific studies [11] and our own community voting data show that ratings for the same face rarely differ by more than 1 point. Beauty is more objective than we often admit.
The "Averageness" Advantage
Our data shows a strong clustering of scores around the 5.0–7.0 range, matching the scientific principle that "average" faces (in terms of mathematical mean) are often rated most attractive [2][3]. The Lookrank AI, trained on large datasets, reflects this bias: extreme deviations from the norm (even unique features) often result in lower scores, while balanced, symmetric faces score higher.
Why the Top 10% Score > 8.2
Scientific studies suggest that small "sweet spot" adjustments like slightly fuller lips or higher cheekbones can shift your attractiveness rating by 1–2 points [6]. This explains the steep drop-off in our distribution above 8.0. Getting a score in the "9+ Club" requires a rare combination of high symmetry [1] and these specific "super-normal" feature traits.
Skin Quality vs. Bone Structure
While bone structure is hard to change, our data supports findings that skin homogeneity (evenness) is a major variable. Improvement in skin texture alone can boost scores [7]. This is a likely factor for users who see score variances under different lighting conditions. The AI detects "radiance" signals similar to the human brain [9].
Debunking the Golden Ratio
Lookrank's scoring model does not strictly adhere to the 1.618 "golden ratio" often peddled by basic scanning apps. Modern research finds it a poor predictor of attractiveness [4]. Instead, our attractiveness test prioritizes feature balance and averageness, which correlates better with human perception [5].
The "Self-Selection" Average (6.20)
Above Average Baseline:
The Lookrank user average of 6.20 is noticeably higher than the theoretical population mean of 5.0. This correlates with the concept that people interested in facial analysis already invest in their appearance (grooming, skincare) [8], pushing the baseline up.
Neural Efficiency:
Highly attractive faces are processed with less "neural effort" [3]. Our AI mirrors this: consistent, clear features are easier to "score" with high confidence, while unique or asymmetric features introduce more variability, often resulting in lower, more conservative scores.
References
How Lookrank rates you:
Lookrank is not a simple image classifier. Our scoring engine uses an ensemble of specialized AI models trained on distinct aesthetic dimensions like facial harmony, skin health, and geometric balance.
RLHF Refinement
Our models have undergone thousands of iterations using Reinforcement Learning from Human Feedback (RLHF). This aligns raw data with human perception so scores reflect real-world attractiveness.
Other "Looksmaxing apps"
Unlike competitors that are just thin "wrappers" around generic APIs, our system uses custom datasets curated for facial aesthetics. We analyze features in context for better accuracy.