# End-to-end Evaluation of Practical Video Analytics Systems for Face Detection and Recognition

arXiv: https://arxiv.org/abs/2310.06945 · HTML: https://mlsanity.com/paper/2310.06945/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices](https://mlsanity.com/paper/1804.07573/) (2018)
- [Partial FC: Training 10 Million Identities on a Single Machine](https://mlsanity.com/paper/2010.05222/) (2020)
- [Sample and Computation Redistribution for Efficient Face Detection](https://mlsanity.com/paper/2105.04714/) (2021)
- [End-To-End Face Detection and Recognition](https://mlsanity.com/paper/1703.10818/) (2017)

## Abstract (arXiv)

Practical video analytics systems that are deployed in bandwidth constrained environments like autonomous vehicles perform computer vision tasks such as face detection and recognition. In an end-to-end face analytics system, inputs are first compressed using popular video codecs like HEVC and then passed onto modules that perform face detection, alignment, and recognition sequentially. Typically, the modules of these systems are evaluated independently using task-specific imbalanced datasets that can misconstrue performance estimates. In this paper, we perform a thorough end-to-end evaluation of a face analytics system using a driving-specific dataset, which enables meaningful interpretations. We demonstrate how independent task evaluations, dataset imbalances, and inconsistent annotations can lead to incorrect system performance estimates. We propose strategies to create balanced evaluation subsets of our dataset and to make its annotations consistent across multiple analytics tasks and scenarios. We then evaluate the end-to-end system performance sequentially to account for task interdependencies. Our experiments show that our approach provides consistent, accurate, and interpretable estimates of the system's performance which is critical for real-world applications.