Compact Convolutional Neural Network Cascade for Face Detection

TL;DR

This paper introduces a compact convolutional neural network cascade for face detection that achieves real-time performance on high-resolution videos with high efficiency and low computational cost, suitable for mobile platforms.

Contribution

The paper presents a novel, highly efficient CNN cascade for face detection that outperforms existing algorithms in speed while maintaining competitive accuracy.

Findings

Real-time detection of 4K Ultra HD videos at 27 fps.

High computational efficiency enables deployment on mobile devices.

Detection performance is robust to background complexity and object count.

Abstract

The problem of faces detection in images or video streams is a classical problem of computer vision. The multiple solutions of this problem have been proposed, but the question of their optimality is still open. Many algorithms achieve a high quality face detection, but at the cost of high computational complexity. This restricts their application in the real-time systems. This paper presents a new solution of the frontal face detection problem based on compact convolutional neural networks cascade. The test results on FDDB dataset show that it is competitive with state-of-the-art algorithms. This proposed detector is implemented using three technologies: SSE/AVX/AVX2 instruction sets for Intel CPUs, Nvidia CUDA, OpenCL. The detection speed of our approach considerably exceeds all the existing CPU-based and GPU-based algorithms. Because of high computational efficiency, our detector can processing 4K Ultra HD video stream in real time (up to 27 fps) on mobile platforms (Intel Ivy Bridge CPUs and Nvidia Kepler GPUs) in searching objects with the dimension 60x60 pixels or higher. At the same time its performance weakly dependent on the background and number of objects in scene. This is achieved by the asynchronous computation of stages in the cascade.