2015/08/06 by Ilya Kalinovskii, Kalinovskii, Ilya, В.Г. Спицын +2
Computer Science · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1508.01292
Demo video and test results available at http://github.com/Bkmz21/FD-Evaluation
openalex publication_date 2015/08/06 · arxiv created 2015/11/23 · arxiv updated 2015/11/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.