# Unconstrained Face Verification using Deep CNN Features

arXiv: https://arxiv.org/abs/1508.01722 · HTML: https://mlsanity.com/paper/1508.01722/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2015.

## Built on by (most cited first, 13 of 13)

- [GeneCIS: A Benchmark for General Conditional Image Similarity](https://mlsanity.com/paper/2306.07969/) (2023)
- [LARNet: Lie Algebra Residual Network for Face Recognition](https://mlsanity.com/paper/2103.08147/) (2021)
- [Open-Set Face Recognition with Maximal Entropy and Objectosphere Loss](https://mlsanity.com/paper/2311.00400/) (2023)
- [Deep face recognition with clustering based domain adaptation](https://mlsanity.com/paper/2205.13937/) (2022)
- [Information Maximization for Extreme Pose Face Recognition](https://mlsanity.com/paper/2209.03456/) (2022)
- [Pose Attention-Guided Profile-to-Frontal Face Recognition](https://mlsanity.com/paper/2209.07001/) (2022)
- [Imponderous Net for Facial Expression Recognition in the Wild](https://mlsanity.com/paper/2103.15136/) (2021)
- [Adaptive Face Recognition Using Adversarial Information Network](https://mlsanity.com/paper/2305.13605/) (2023)
- [Inclusive normalization of face images to passport format](https://mlsanity.com/paper/2312.14544/) (2023)
- [Profile to Frontal Face Recognition in the Wild Using Coupled Conditional GAN](https://mlsanity.com/paper/2107.13742/) (2021)
- [Human-Machine Comparison for Cross-Race Face Verification: Race Bias at the Upper Limits of Performance?](https://mlsanity.com/paper/2305.16443/) (2023)
- [Unmasking the Uniqueness: A Glimpse into Age-Invariant Face Recognition of Indigenous African Faces](https://mlsanity.com/paper/2408.06806/) (2024)
- [Multi-Level Evidence Aggregation for Robust Facial Phenotype Retrieval in Rare Genetic Disorder Prioritization](https://mlsanity.com/paper/2608.11037/) (2026)

## 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.