Conventional subspace learning-based face recognition aims to attain low recognition errors and assumes same loss from all misclassifications. In many real-world face recognition...
Abstract--Dimensionality reduction methods have been successfully employed for face recognition. Among the various dimensionality reduction algorithms, linear (Fisher) discriminant...
Two-dimensional principal component analysis (2DPCA) is based on the 2D images rather than 1D vectorized images like PCA, which is a classical feature extraction technique in face...
Government agencies are investing a considerable amount of resources into improving security systems as result of recent terrorist events that dangerously exposed flaws and weakn...
Andrea F. Abate, Michele Nappi, Daniel Riccio, Gab...
Illumination variation that occurs on face images degrades the performance of face recognition. In this paper, we propose a novel approach to handling illumination variation for f...
Detecting faces in images is a key step in numerous computer vision applications, such as face recognition or facial expression analysis. Automatic face detection is a difficult ...
Face recognition is a challenging task in computer vision and pattern recognition. It is well-known that obtaining a low-dimensional feature representation with enhanced discrimin...
Fei Wang, Jingdong Wang, Changshui Zhang, James T....
Feature extraction is among the most important problems in face recognition systems. In this paper, we propose an enhanced kernel discriminant analysis (KDA) algorithm called kern...
Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is th...
Mrinal Kanti Bhowmik, Debotosh Bhattacharjee, Mita...
This paper presents an e cient automatic face recognition scheme useful for video indexing applications. In particular7 the following problem is addressed: given a set of known fa...