# DeepFace: Face Generation using Deep Learning

arXiv: https://arxiv.org/abs/1701.01876 · HTML: https://mlsanity.com/paper/1701.01876/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2017.

## Built on by (most cited first, 2 of 2)

- [FairCoT: Enhancing Fairness in Text-to-Image Generation via Chain of Thought Reasoning with Multimodal Large Language Models](https://mlsanity.com/paper/2406.09070/) (2024)
- [SIG: A Synthetic Identity Generation Pipeline for Generating Evaluation Datasets for Face Recognition](https://mlsanity.com/paper/2409.08345/) (2024)

## Abstract (arXiv)

We use CNNs to build a system that both classifies images of faces based on a variety of different facial attributes and generates new faces given a set of desired facial characteristics. After introducing the problem and providing context in the first section, we discuss recent work related to image generation in Section 2. In Section 3, we describe the methods used to fine-tune our CNN and generate new images using a novel approach inspired by a Gaussian mixture model. In Section 4, we discuss our working dataset and describe our preprocessing steps and handling of facial attributes. Finally, in Sections 5, 6 and 7, we explain our experiments and results and conclude in the following section. Our classification system has 82% test accuracy. Furthermore, our generation pipeline successfully creates well-formed faces.