International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar -2017
p-ISSN: 2395-0072
www.irjet.net
Automated Face Recognition System for Criminals via Dictionary Learning Manisha D. Sakharkar, Tubasamer M. Pathan, Dipika D. Kailsait, Arti S. Kailuke Manisha D. Sakharkar Student, Dept. of CSE Engineering, DES’s COET, Dhamangaon Rly, Amravati, Maharashtra, India Tubasamer M. Pathan Student, Dept. of CSE Engineering, DES’s COET, Dhamangaon Rly, Amravati, Maharashtra, India Dipika D. Kalsait Student, Dept. of CSE Engineering, DES’s COET, Dhamangaon Rly, Amravati, Maharashtra, India Arti S. Kailuke Student, Dept. of CSE Engineering, DES’s COET, Dhamangaon Rly, Amravati, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract- The paper deals with the face recognition of criminals from the images and videos. It is the undersampled method because it does not uses much images to perform the face recognition. Now-a-days crimes are increasing at it is difficult for the police to maintain their records and to handle them. So with the help of images and video they can perform the recognition. It allows the easy recognition without maintaining the paper record. We address the problem of robust face recognition with undersampled training data. Given only one or few training images available per subject, we present a novel recognition approach. Key Words: Undersampled, face recognition, subjects, robust, training data.
1. INTRODUCTION Face recognition has been an active research topic, since it is difficult to recognize face images with illumination and expression variations as well as corruptions due to occlusion or disguise. A general solution is to collect a sufficient amount of training data in advance, so that the above intraclass variations can be properly handled. However, in practice, there is no guarantee that we get the satisfactory result. Moreover, for real-world applications, e.g. e-passport, driving license, or ID card identification, only one or very few face images of the subject of interest might be captured during the data acquisition stage. As a result, one would encounter the challenging task of undersampled face recognition.
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Existing solutions to undersampled face recognition can be typically divided into two categories: patch-based methods and generic learning from external data. For patchbased methods, one can either extract discriminative information from patches collected by different images, or utilize/integrate the corresponding classification results for achieving recognition.
2. RELATED WORK There are different techniques for face recognition. SRC and extended SRC is used in our work.
2.1 SRC The problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. The recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1minimization, SRC propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as Eigen faces and Laplacian faces, as long as ISO 9001:2008 Certified Journal
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