From 1970, research on automated face recognition has been on the rise. Since then many techniques and algorithms have been designed each one trying to provide better efficiency than the earlier one. This field of biometric analysis has found its use in many practical applications and with rising technologies each day, its exhaustive use in future is also expected. In this paper we have studied...

In the domain of object recognition, the SIFT feature [1] is known to be a very successful local invariant feature. The performance of the recognition task using SIFT features is very robust and also can be done in real-time. This project present an approach that adopt the SIFT feature for the task of face detection. A feature database is created for the detection of generic face features and a...

Growth of videos in today’s Internet usage is extensive. Different types of videos will be available in the Internet which among them are lecture videos. Students can make use of these videos, so there is a need to develop an automated system to search the required content only, rather than wasting the time in viewing the complete video. This can be developed into automated system, required ste...

In 2004, David G. Lowe published his paper “Distinctive Image Features from ScaleInvariant Keypoints” (Lowe, 2004, [2]), outlining a method he developed for finding distinctive, scale and rotation invariant features in images that can be used to perform matching between different views of an object or scene. His method, Scale-Invariant Feature Transform (SIFT) combines scale-space theory and fe...

This paper presents a new face identification system based on Graph Matching Technique on SIFT features extracted from face images. Although SIFT features have been successfully used for general object detection and recognition, only recently they were applied to face recognition. This paper further investigates the performance of identification techniques based on Graph matching topology drawn...

Scale Invariant Feature Transform (SIFT) is a popular image feature extraction algorithm. SIFT’s features are invariant to many image related variables including scale and change in viewpoint. Despite its broad capabilities, it is computationally expensive. This characteristic makes it hard for researchers to use SIFT in their works especially in real time application. This is a common problem ...

If a physical object has a smooth or piecewise smooth boundary, its images obtained by cameras in varying positions undergo smooth apparent deformations. These deformations are locally well approximated by affine transforms of the image plane. In consequence the solid object recognition problem has often been led back to the computation of affine invariant image local features. Such invariant f...

Representing and understanding the three-dimensional (3D) structural information of protein-ligand complexes is a critical step in the rational drug discovery process. Traditional analysis methods are proving inadequate and inefficient in dealing with the massive amount of structural information being generated from X-ray crystallography, NMR, and in silico approaches such as structure-based do...

The chosen solution is based on a client-server architecture and the object recognition is based on local features. The study focuses on the comparison, in terms of time and performance, of the Scale-Invariant Feature Transform (SIFT), the Speeded Up Robust Features (SURF), the Nearest Neighbor Search (NNS) match and a k-means trees based search. It was found that SIFT outperforms SURF in terms...

In this paper, the well-known SIFT detector is extended with a bivariate feature localization. This is done by using function models that assume a Gaussian feature shape for the detected features. As function models we propose (a) a bivariate Gaussian and (b) a Difference of Gaussians. The proposed detector has all properties of SIFT, but provides invariance to affine transformations and blurri...