# A Deep Face Identification Network Enhanced by Facial Attributes Prediction

arXiv: https://arxiv.org/abs/1805.00324 · HTML: https://mlsanity.com/paper/1805.00324/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2018.

## Built on by (most cited first, 7 of 7)

- [Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition](https://mlsanity.com/paper/2303.12512/) (2023)
- [Tasks Structure Regularization in Multi-Task Learning for Improving Facial Attribute Prediction](https://mlsanity.com/paper/2108.04353/) (2021)
- [Deep adversarial attack on target detection systems](https://mlsanity.com/paper/2108.05948/) (2021)
- [Profile to Frontal Face Recognition in the Wild Using Coupled Conditional GAN](https://mlsanity.com/paper/2107.13742/) (2021)
- [Attribute Guided Sparse Tensor-Based Model for Person Re-Identification](https://mlsanity.com/paper/2108.04352/) (2021)
- [EasyPortrait -- Face Parsing and Portrait Segmentation Dataset](https://mlsanity.com/paper/2304.13509/) (2023)
- [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)

In this paper, we propose a new deep framework which predicts facial attributes and leverage it as a soft modality to improve face identification performance. Our model is an end to end framework which consists of a convolutional neural network (CNN) whose output is fanned out into two separate branches; the first branch predicts facial attributes while the second branch identifies face images. Contrary to the existing multi-task methods which only use a shared CNN feature space to train these two tasks jointly, we fuse the predicted attributes with the features from the face modality in order to improve the face identification performance. Experimental results show that our model brings benefits to both face identification as well as facial attribute prediction performance, especially in the case of identity facial attributes such as gender prediction. We tested our model on two standard datasets annotated by identities and face attributes. Experimental results indicate that the proposed model outperforms most of the current existing face identification and attribute prediction methods.