model based approach is used. In this approach the Moment based feature extraction techniques (Hu’s Moments and Zernike Moments) are implemented on two different face databases containing different poses of the face. Extracted feature are classified by kNN classifier and Correct Recognition Rate

Abstract — Illumination and pose variations that occur on face images degrade the performance of face recognition. In this paper, we propose a novel approach for handling illumination and pose variations for face recognition simultaneously. We use the two-dimensional view-based face recognition

a comparison between the local method Elastic Bunch Graph Matching, the global approaches principle component analysis (PCA) and PCA with linear discriminant analysis (PCA+LDA). The outcome of this study is that while in cases of small face pose variations Elastic Bunch Graph Matching works slightly

specifically against pose variations. Facilitated with 3D face dataset, the process of face averaging is tested on face images free from human errors and misalignments. Single images are chosen as gallery and the averaged views as probe in all the experiments. Three different scenarios are experimented, i

#culty in face recognition is the separation of intrinsic facial characteristics from extrinsic image variations. Among the latter in 2D images is pose, illumination, and expression. Unfortunately, most past studies have provided variation-speci#c solutions that are not applicable to other types of variation

We present a generative appearance-based method for recognizing human faces under variation in lighting and viewpoint. Our method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the space of images. Using a

be met. To accommodate the pose problem, this study integrates two-dimensional morphing with pose estimation. Two-dimensional morphing transforms various poses of a face image into a frontal view, thereby eliminating the center bias and pose variations and increasing the tolerance for pose estimation

"The fascination of mathematics is fundamentally the same as the fascination of exploration except that the discoveries are made in the realm of ideas rather than in physical space. No doubt the pleasure is greatest when an idea is clarified and isolated after a struggle, but most people have sufficient experience, if only in attempting to solve Christmas puzzles, to enable them to understand the exuberance with which Pythagoras and Archimedes are said to have celebrated their discoveries. It is not possible for our pupils to rediscover the whole of mathematics for themselves, or even those portions of it which seem to the greatest relevance today, but fortunately the pleasure seems to be experienced under guided discovery. It is important that the classroom activities should be carried on with a certain degree of expectancy; new ideas, fresh discoveries, deepened interest are just round the corner waiting to burst in at any moment. " - Bailey et al (1974: 148) How is new mathematics discovered or created? Where does this theorem or

A common trend in object recognition is to detect and leverage the use of sparse, informative feature points. The use of such features makes the problem more manageable while providing increased robustness to noise and pose variation. In this work we develop an extension of these ideas

are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds. 1