Learning Convolutional Domain-Robust Representations for Cross-View Face Recognition
نویسندگان
چکیده
منابع مشابه
Robust multi-camera view face recognition
This paper presents multi-appearance fusion of Principal Component Analysis (PCA) and generalization of Linear Discriminant Analysis (LDA) for multi-camera view offline face recognition (verification) system. The generalization of LDA has been extended to establish correlations between the face classes in the transformed representation and this is called canonical covariate. The proposed system...
متن کاملBackground Learning for Robust Face Recognition
In this paper, we propose a robust face recognition technique based on the principle of eigenfaces. The traditional eigenface recognition (EFR) method works quite well when the input test patterns are cropped faces. However, when confronted with recognizing faces embedded in arbitrary backgrounds, the EFR method fails to discriminate effectively between faces and background patterns, giving ris...
متن کاملAn Information-Theoretic Discussion of Convolutional Bottleneck Features for Robust Speech Recognition
Convolutional Neural Networks (CNNs) have been shown their performance in speech recognition systems for extracting features, and also acoustic modeling. In addition, CNNs have been used for robust speech recognition and competitive results have been reported. Convolutive Bottleneck Network (CBN) is a kind of CNNs which has a bottleneck layer among its fully connected layers. The bottleneck fea...
متن کاملRobust representations for face recognition: the power of averages.
We are able to recognise familiar faces easily across large variations in image quality, though our ability to match unfamiliar faces is strikingly poor. Here we ask how the representation of a face changes as we become familiar with it. We use a simple image-averaging technique to derive abstract representations of known faces. Using Principal Components Analysis, we show that computational sy...
متن کاملDiscriminative Local Sparse Representations for Robust Face Recognition
A key recent advance in face recognition models a test face image as a sparse linear combination of a set of training face images. The resulting sparse representations have been shown to possess robustness against a variety of distortions like random pixel corruption, occlusion and disguise. This approach however makes the restrictive (in many scenarios) assumption that test faces must be perfe...
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: IEICE Transactions on Information and Systems
سال: 2014
ISSN: 0916-8532,1745-1361
DOI: 10.1587/transinf.2014edl8095