# Accurate and Efficient Similarity Search for Large Scale Face Recognition

Source: [Accurate and Efficient Similarity Search for Large Scale Face Recognition](<https://www.sota2.com/research/paper/68fa646c1e209131cea04a10>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:04:03.574Z

Authors: Ce Qi, Zhizhong Liu, Fei Su

Published: 2018-06-01T14:13:56.000Z

## Abstract

Face verification is a relatively easy task with the help of discriminative features from deep neural networks. However, it is still a challenge to recognize faces on millions of identities while keeping high performance and efficiency. The challenge 2 of MS-Celeb-1M is a classification task. However, the number of identities is too large and it is not that elegant to treat the task as an image classification task. We treat the classification task as similarity search and do experiments on different similarity search strategies. Similarity search strategy accelerates the speed of searching and boosts the accuracy of final results. The model used for extracting features is a single deep neural network pretrained on CASIA-Webface, which is not trained on the base set or novel set offered by official. Finally, we rank \\textbf{3rd}, while the speed of searching is 1ms/image.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1806.00365>)

[PDF](<https://arxiv.org/pdf/1806.00365.pdf>)