publication . Part of book or chapter of book . Other literature type . Preprint . 2018

MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices

Chen, Sheng; Liu, Yang; Gao, Xiang; Han, Zhen;
Open Access
  • Published: 20 Apr 2018
  • Publisher: Springer International Publishing
Abstract
We present a class of extremely efficient CNN models, MobileFaceNets, which use less than 1 million parameters and are specifically tailored for high-accuracy real-time face verification on mobile and embedded devices. We first make a simple analysis on the weakness of common mobile networks for face verification. The weakness has been well overcome by our specifically designed MobileFaceNets. Under the same experimental conditions, our MobileFaceNets achieve significantly superior accuracy as well as more than 2 times actual speedup over MobileNetV2. After trained by ArcFace loss on the refined MS-Celeb-1M, our single MobileFaceNet of 4.0MB size achieves 99.55%...
Subjects
free text keywords: Mobile phone, Computer engineering, Convolutional neural network, Speedup, Inference, Computer science, Mobile device, Facial recognition system, Deep learning, Cellular network, Artificial intelligence, business.industry, business, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Learning
Related Organizations
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue