Minimum margin loss for deep face recognition
نویسندگان
چکیده
منابع مشابه
CosFace: Large Margin Cosine Loss for Deep Face Recognition
Face recognition has made extraordinary progress owing to the advancement of deep convolutional neural networks (CNNs). The central task of face recognition, including face verification and identification, involves face feature discrimination. However, the traditional softmax loss of deep CNNs usually lacks the power of discrimination. To address this problem, recently several loss functions su...
متن کاملArcFace: Additive Angular Margin Loss for Deep Face Recognition
Convolutional neural networks have significantly boosted the performance of face recognition in recent years due to its high capacity in learning discriminative features. To enhance the discriminative power of the Softmax loss, multiplicative angular margin [23] and additive cosine margin [44, 43] incorporate angular margin and cosine margin into the loss functions, respectively. In this paper,...
متن کاملRange Loss for Deep Face Recognition with Long-tail
Convolutional neural networks have achieved great improvement on face recognition in recent years because of its extraordinary ability in learning discriminative features of people with different identities. To train such a welldesigned deep network, tremendous amounts of data is indispensable. Long tail distribution specifically refers to the fact that a small number of generic entities appear...
متن کاملOrthogonal Maximum Margin Projection for Face Recognition
Dimensionality reduction techniques that can introduce low-dimensional feature representation with enhanced discriminatory power are of paramount importance in face recognition. In this paper, a novel subspace learning algorithm called orthogonal maximum margin projection(OMMP) is proposed. The OMMP algorithm is based on the maximum margin projection (MMP), which aims at discovering both geomet...
متن کاملDomain Adaptation with Soft-margin multiple feature-kernel learning beats Deep Learning for surveillance face recognition
Face recognition (FR) is the most preferred mode for biometric-based surveillance, due to its passive nature of detecting subjects, amongst all different types of biometric traits. FR under surveillance scenario does not give satisfactory performance due to low contrast, noise and poor illumination conditions on probes, as compared to the training samples. A state-of-the-art technology, Deep Le...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2020
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2019.107012