Face Density as a Proxy for Data Complexity: Quantifying the Hardness of Instance Count
Machine learning progress has historically prioritized model-centric innovations, yet achievable performance is frequently capped by the intrinsic complexity of the data itself. In this work, we isolate and quantify the impact of instance density (measured by face count) as a primary driver of data complexity. Rather than simply observing that ``crowded scenes are harder,'' we rigorously control for class imbalance to measure the precise degradation caused by density alone. Controlled experiments on the WIDER FACE and Open Images datasets, restricted to exactly 1 to 18 faces per image with perfectly balanced sampling, reveal that model performance degrades monotonically with increasing fa...
| Authors | Abolfazl Mohammadi-Seif, Ricardo Baeza-Yates |
|---|---|
| Primary category | cs.CV |
| Published | 6 April 2026 |
| Last updated at source | 14 April 2026 |
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