# A Fast Face Detection Method via Convolutional Neural Network

## TL;DR

This paper introduces a rapid face detection approach using a specially designed CNN that extracts discriminative complete features, eliminating the need for multi-scale image pyramids and significantly boosting detection speed.

## Contribution

The paper presents a novel CNN-based face detection method utilizing discriminative complete features that achieve high speed without multi-scale image pyramids, improving efficiency.

## Key findings

- Achieves faster face detection compared to traditional multi-scale methods.
- Demonstrates high detection accuracy on popular datasets.
- Reduces computational complexity significantly.

## Abstract

Current face or object detection methods via convolutional neural network (such as OverFeat, R-CNN and DenseNet) explicitly extract multi-scale features based on an image pyramid. However, such a strategy increases the computational burden for face detection. In this paper, we propose a fast face detection method based on discriminative complete features (DCFs) extracted by an elaborately designed convolutional neural network, where face detection is directly performed on the complete feature maps. DCFs have shown the ability of scale invariance, which is beneficial for face detection with high speed and promising performance. Therefore, extracting multi-scale features on an image pyramid employed in the conventional methods is not required in the proposed method, which can greatly improve its efficiency for face detection. Experimental results on several popular face detection datasets show the efficiency and the effectiveness of the proposed method for face detection.