Local invariant key point extraction has recently emerged as an attractive approach for detecting near duplicate images. Near duplicate images can be: (i) perceptually identical images (e.g. allowing for change in color balance, change in brightness, compression artifacts, contrast adjustment, rotation, cropping, filtering, scaling etc.), (ii) images of the same 3D scene (from different viewpoints). The requirements for identifying near duplicate images vary according to the application. In this paper we focus on image matching strategy that will assist in the detection of forged (copy-paste forgery) images. So far, no specific image matching strategy exists for this application. The state of the art methodologies tend to generate many false positives. In this paper we have introduced a novel matching strategy for pattern matching of key point distributions. Typical experiments conducted with real world images demonstrate success in near duplicate image retrieval for the application of digital image forensic. Proposed method outperforms some of the existing methods and is computationally efficient.
Editing on digital images is ubiquitous. Identification of deliberately modified facial images is a new challenge for face identification system. In this paper, we address the problem of identification of a face or person from heavily altered facial images. In this face identification problem, the input to the system is a manipulated or transformed face image and the system reports back the determined identity from a database of known individuals. Such a system can be useful in mugshot identification in which mugshot database contains two views (frontal and profile) of each criminal. We considered only frontal view from the available database for face identification and the query image is a manipulated face generated by face transformation software tool available online. We propose SIFT features for efficient face identification in this scenario. Further comparative analysis has been given with well known eigenface approach. Experiments have been conducted with real case images to evaluate the performance of both methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.