# Face Attribute Prediction Using Off-the-Shelf CNN Features

arXiv: https://arxiv.org/abs/1602.03935 · HTML: https://mlsanity.com/paper/1602.03935/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2016.

## Built on by (most cited first, 10 of 10)

- [CelebV-HQ: A Large-Scale Video Facial Attributes Dataset](https://mlsanity.com/paper/2207.12393/) (2022)
- [OvarNet: Towards Open-vocabulary Object Attribute Recognition](https://mlsanity.com/paper/2301.09506/) (2023)
- [Multi-task head pose estimation in-the-wild](https://mlsanity.com/paper/2202.02299/) (2022)
- [Improving Facial Attribute Recognition by Group and Graph Learning](https://mlsanity.com/paper/2105.13825/) (2021)
- [Attribute-Based Deep Periocular Recognition: Leveraging Soft Biometrics to Improve Periocular Recognition](https://mlsanity.com/paper/2111.01325/) (2021)
- [Tasks Structure Regularization in Multi-Task Learning for Improving Facial Attribute Prediction](https://mlsanity.com/paper/2108.04353/) (2021)
- [Facial Data Minimization: Shallow Model as Your Privacy Filter](https://mlsanity.com/paper/2310.15590/) (2023)
- [Variational Self-Supervised Contrastive Learning Using Beta Divergence](https://mlsanity.com/paper/2312.00824/) (2023)
- [Revisiting Marr in Face: The Building of 2D--2.5D--3D Representations in Deep Neural Networks](https://mlsanity.com/paper/2411.16148/) (2024)
- [The Good, the Better, and the Best: Improving the Discriminability of Face Embeddings through Attribute-aware Learning](https://mlsanity.com/paper/2603.15062/) (2026)

## Abstract (arXiv)

Predicting attributes from face images in the wild is a challenging computer vision problem. To automatically describe face attributes from face containing images, traditionally one needs to cascade three technical blocks --- face localization, facial descriptor construction, and attribute classification --- in a pipeline. As a typical classification problem, face attribute prediction has been addressed using deep learning. Current state-of-the-art performance was achieved by using two cascaded Convolutional Neural Networks (CNNs), which were specifically trained to learn face localization and attribute description. In this paper, we experiment with an alternative way of employing the power of deep representations from CNNs. Combining with conventional face localization techniques, we use off-the-shelf architectures trained for face recognition to build facial descriptors. Recognizing that the describable face attributes are diverse, our face descriptors are constructed from different levels of the CNNs for different attributes to best facilitate face attribute prediction. Experiments on two large datasets, LFWA and CelebA, show that our approach is entirely comparable to the state-of-the-art. Our findings not only demonstrate an efficient face attribute prediction approach, but also raise an important question: how to leverage the power of off-the-shelf CNN representations for novel tasks.