# Convolutional Neural Network Based Partial Face Detection

arXiv: https://arxiv.org/abs/2206.14350 · HTML: https://mlsanity.com/paper/2206.14350/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 29 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [Understanding and Comparing Deep Neural Networks for Age and Gender Classification](https://mlsanity.com/paper/1708.07689/) (2017)
- [Multi-view Face Detection Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1502.02766/) (2015)

## Built on by (most cited first, 3 of 3)

- [Predictive Modeling for Breast Cancer Classification in the Context of Bangladeshi Patients: A Supervised Machine Learning Approach with Explainable AI](https://mlsanity.com/paper/2404.04686/) (2024)
- [Machine Learning Approach on Multiclass Classification of Internet Firewall Log Files](https://mlsanity.com/paper/2306.07997/) (2023)
- [Cannabis Seed Variant Detection using Faster R-CNN](https://mlsanity.com/paper/2403.10722/) (2024)

## Abstract (arXiv)

Due to the massive explanation of artificial intelligence, machine learning technology is being used in various areas of our day-to-day life. In the world, there are a lot of scenarios where a simple crime can be prevented before it may even happen or find the person responsible for it. A face is one distinctive feature that we have and can differentiate easily among many other species. But not just different species, it also plays a significant role in determining someone from the same species as us, humans. Regarding this critical feature, a single problem occurs most often nowadays. When the camera is pointed, it cannot detect a person's face, and it becomes a poor image. On the other hand, where there was a robbery and a security camera installed, the robber's identity is almost indistinguishable due to the low-quality camera. But just making an excellent algorithm to work and detecting a face reduces the cost of hardware, and it doesn't cost that much to focus on that area. Facial recognition, widget control, and such can be done by detecting the face correctly. This study aims to create and enhance a machine learning model that correctly recognizes faces. Total 627 Data have been collected from different Bangladeshi people's faces on four angels. In this work, CNN, Harr Cascade, Cascaded CNN, Deep CNN & MTCNN are these five machine learning approaches implemented to get the best accuracy of our dataset. After creating and running the model, Multi-Task Convolutional Neural Network (MTCNN) achieved 96.2% best model accuracy with training data rather than other machine learning models.