Discriminative sparse representation for face recognition
نویسندگان
چکیده
منابع مشابه
Sparse Representation for Face Recognition
Sparse representation has attracted a great deal of attention in the past decade. Famous transforms such as discrete Fourier transform, wavelet transform and singular value decomposition are used to sparsely represent the signals. The aim of these transforms is to reveal certain structures of a signal and representation of these structures in a compact form. Therefore, sparse representation pro...
متن کاملDiscriminative Sparse Representation for Expression Recognition
This thesis is focused on recognising emotions of different subjects through facial expressions in 2D images. We will go through the multiple stages of this problem where we aim to take maximum advantage of supervised algorithms and labelled information. We will compare different pixel processing techniques and show that the histogram based ones, like HOG and LBP, have the best performance for ...
متن کاملFace Recognition Using Sparse Representation
Many classic and contemporary face recognition algorithms work well on public data sets, but degrade sharply when they are used in a real recognition system. This is mostly due to the difficulty of simultaneously handling variations in illumination, image misalignment, and occlusion in the test image. We consider a scenario where the training images are well controlled and test images are only ...
متن کاملSparse Representation for Video-Based Face Recognition
With the ever-increasing security threats, the problem of invulnerable authentication systems is becoming more acute. Traditional means of securing a facility essentially depend on strategies corresponding to “what you have” or “what you know”, for example smart cards, keys and passwords. These systems however can easily be fooled. Passwords for example, are difficult to remember and therefore ...
متن کاملCompetitive Sparse Representation Classification for Face Recognition
A method, named competitive sparse representation classification (CSRC), is proposed for face recognition in this paper. CSRC introduces a lowest competitive deletion mechanism which removes the lowest competitive sample based on the competitive ability of training samples for representing a probe in multiple rounds collaborative linear representation. In other words, in each round of competing...
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: Multimedia Tools and Applications
سال: 2015
ISSN: 1380-7501,1573-7721
DOI: 10.1007/s11042-015-3136-x