Minimum Margin Loss for Deep Face Recognition
arXiv:1805.06741 · doi:10.1016/j.patcog.2019.107012
Abstract
Face recognition has achieved great progress owing to the fast development of the deep neural network in the past a few years. As an important part of deep neural networks, a number of the loss functions have been proposed which significantly improve the state-of-the-art methods. In this paper, we proposed a new loss function called Minimum Margin Loss (MML) which aims at enlarging the margin of those overclose class centre pairs so as to enhance the discriminative ability of the deep features. MML supervises the training process together with the Softmax Loss and the Centre Loss, and also makes up the defect of Softmax + Centre Loss. The experimental results on MegaFace, LFW and YTF datasets show that the proposed method achieves the state-of-the-art performance, which demonstrates the effectiveness of the proposed MML.
References in corpus (15)
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
- Deep Learning Face Representation by Joint Identification-Verification
- Learning Face Representation from Scratch
- Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors
- Additive Margin Softmax for Face Verification
- Large-Margin Softmax Loss for Convolutional Neural Networks
- SphereFace: Deep Hypersphere Embedding for Face Recognition
- Targeting Ultimate Accuracy: Face Recognition via Deep Embedding
- Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
- MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
- Do We Really Need to Collect Millions of Faces for Effective Face Recognition?
- Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation
- Learning a Metric Embedding for Face Recognition using the Multibatch Method
- Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition