mlsanityData to 7 Oct 2026

Face Detection using Deep Learning: An Improved Faster RCNN Approach

arXiv 1701.08289 ↗First posted 28 Jan 2017cs.CVcited by 16

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Abstract (arXiv)

In this report, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detetion benchmark evaluation. In particular, we improve the state-of-the-art faster RCNN framework by combining a number of strategies, including feature concatenation, hard negative mining, multi-scale training, model pretraining, and proper calibration of key parameters. As a consequence, the proposed scheme obtained the state-of-the-art face detection performance, making it the best model in terms of ROC curves among all the published methods on the FDDB benchmark.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jan 2017.

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Citing papers per year: 2021: 6; 2022: 4; 2023: 2; 2024: 3; 2025: 1.

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