Papers › Unconstrained Face Verification using Deep CNN Features
Unconstrained Face Verification using Deep CNN Features
Jun-Cheng Chen, Vishal M. Patel, Rama Chellappa
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Face Verification | IJB-A | DCNN | TAR @ FAR=0.01 | 83.80% | #14 of 17 | Archive leaderboard | report |
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