What it does
This KPI uses advanced face detection technology to detect faces in marketing assets.
Why it matters
Faces naturally attract the viewer's attention, making them key to engaging audiences. Effective use of human faces in assets can significantly increase ad memorability, emotional connection, and audience engagement (including on social platforms).
How it works
The image is analyzed by a deep learning model trained to detect human faces. For each image it returns the location of a face detected at or above a confidence level.
How to achieve great results
- Ensure diverse and emotionally expressive faces are included in assets to increase engagement and relatability.
- Align the portrayal of individuals in assets with the brand's target demographic and core values.
- Strategically use well-known personalities to increase brand association and recall.
AI models used
Face detection models at aimpower are based on the latest deep learning frameworks used in scientific literature for the purpose of memorability modelling, including convolutional deep learning networks and vision transformers. The images used to collect human ground truth data originate from various sources, including social media platforms, large scale webscrapes (Common Crawl) and marketing materials (e.g. ads).
In total we leverage 1m+ images for pretraining, and 32K+ images with ~400,000 human annotated faces for fine-tuning our models on the face detection task. This training enables the model to accurately recognize and analyze faces across different scales, poses, expressions, occlusions, and lighting conditions. Evaluation of our models follows the scientific best practices. Accuracy versus human ground truth data is 99%.