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Unconstrained Face Verification using Deep CNN Features

arXiv 1508.01722 ↗First posted 7 Aug 2015cs.CVcited by 13

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Abstract (arXiv)

In this paper, we present an algorithm for unconstrained face verification based on deep convolutional features and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset. The IJB-A dataset includes real-world unconstrained faces from 500 subjects with full pose and illumination variations which are much harder than the traditional Labeled Face in the Wild (LFW) and Youtube Face (YTF) datasets. The deep convolutional neural network (DCNN) is trained using the CASIA-WebFace dataset. Extensive experiments on the IJB-A dataset are provided.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Aug 2015.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 5; 2024: 1; 2025: 0; 2026: 1.

13 papers.

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