Introduction: The Challenge of Scaling Person-Pair Comparison
In the heart of any person-recognition knowledge base (KB) lies a critical operation: comparing individuals based on their reference embeddings. These embeddings, derived from face and body encodings, serve as the foundation for identifying similarities or overlaps in identity. However, as the KB grows in size and complexity, the computational and memory demands of pairwise comparisons become a bottleneck. This article explores a practical, step-by-step optimization journey from inefficient Python loops to efficient NumPy-based solutions, highlighting the trade-offs between readability, performance, and memory usage.
The Problem: Inefficient Python Loops
Consider a typical scenario where each person in the KB is associated with multiple embedding vectors. The task is to compute the average and minimum distance between all pairs of persons. A naive Python implementation involves nested loops:
Outer loop: Iterate over all person combinations.
