41 research outputs found
Multi-task Image Classification via Collaborative, Hierarchical Spike-and-Slab Priors
Promising results have been achieved in image classification problems by
exploiting the discriminative power of sparse representations for
classification (SRC). Recently, it has been shown that the use of
\emph{class-specific} spike-and-slab priors in conjunction with the
class-specific dictionaries from SRC is particularly effective in low training
scenarios. As a logical extension, we build on this framework for multitask
scenarios, wherein multiple representations of the same physical phenomena are
available. We experimentally demonstrate the benefits of mining joint
information from different camera views for multi-view face recognition.Comment: Accepted to International Conference in Image Processing (ICIP) 201
Face Recognition Via GroupWise Registration Method
One of the important research area in image processing is face recognition. We introduce a new framework for tackling face recognition problem. Here propose a new way technique of face recognition problem, which is formulated as group wise deformable image registration and feature matching. The main contributions of the proposed method is to suppresses image noise without reducing the image sharpness we will use Median filtering, Each pixel in a facial image is represented by an anatomical signature obtained from its corresponding most salient scale local region Based on the anatomical signature calculated from each pixel, a novel Markov random field based group wise registration framework is proposed to formulate the face recognition problem.
DOI: 10.17762/ijritcc2321-8169.150317
Human Face Identification by a Markov Random Field GroupWise Registration Technique
Face recognition is widely used in various applications like in bank applications, at airport or at ATM centre for security purposes etc. There are various methods used for face recognition problem. In this paper I propose new method known as Markov field GroupWise registration in which mean of all the faces from the database will be calculated first and then this mean will be compared with the testing image. To implement these modules, four open source databases like FERET, CAS-PEAL-R1, FRGC ver. 2.0, and the LFW are required. My work will achieve good result as compared to previous methods.
DOI: 10.17762/ijritcc2321-8169.15052