Robust Object-Based Watermarking Using SURF Feature Matching and DFT Domain

In this paper we propose a robust object-based watermarking method, in which the watermark is embedded into the middle frequencies band of the Discrete Fourier Transform (DFT) magnitude of the selected object region, altogether with the Speeded Up Robust Feature (SURF) algorithm to allow the correct watermark detection, even if the watermarked image has been distorted. To recognize the selected object region after geometric distortions, during the embedding process the SURF features are estimated and stored in advance to be used during the detection process. In the detection stage, the SURF features of the distorted image are estimated and match them with the stored ones. From the matching result, SURF features are used to compute the Affine-transformation parameters and the object region is recovered. The quality of the watermarked image is measured using the Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and the Visual Information Fidelity (VIF). The experimental results show the proposed method provides robustness against several geometric distortions, signal processing operations and combined distortions. The receiver operating characteristics (ROC) curves also show the desirable detection performance of the proposed method. The comparison with a previously reported methods based on different techniques is also provided

Recognition using SIFT and its Variants on Improved Segmented Iris

Iris is one of the most reliable biometric traits due to its stability and randomness. Iris is transformed to polar coordinates by the conventional recognition systems. They perform well for the cooperative databases, but the performance deteriorates for the non-cooperative irises. In addition to this, aliasing effect is introduced as a result of transforming iris to polar domain. In this thesis, these issues are addressed by considering annular iris free from noise due to eyelids. This thesis presents several SIFT based methods for extracting distinctive invariant features from iris that can be used to perform reliable matching between different views of an object or scene. After localization of the iris, Scale Invariant Feature Transform (SIFT) is used to extract the local features. The SIFT descriptor is a widely used method for matching image features. But SIFT is found out to be computationally very complex. So we use another keypoint descriptor, Speeded up Robust Features (SURF), which is found to be computationally more efficient and produces better results than the SIFT. Both SIFT and SURF has the problem of false pairing. This has been overcome by using Fourier transform with SIFT (called F-SIFT) to obtain the keypoint descriptor and Phase-Only Correlation for feature matching. F-SIFT was found to have better accuracy than both SIFT and SURF as the problem of false pairing is significantly reduced. We also propose a new method called S-SIFT where we used S Transform with SIFT to obtain the keypoint descriptor for the image and Phase-Only Correlation for the feature matching. In the thesis we provide a comparative analysis of these four methods (SIFT, SURF, F-SIFT, S-SIFT) for feature extraction in iris

Gait recognition based on shape and motion analysis of silhouette contours

This paper presents a three-phase gait recognition method that analyses the spatio-temporal shape and dynamic motion (STS-DM) characteristics of a human subject’s silhouettes to identify the subject in the presence of most of the challenging factors that affect existing gait recognition systems. In phase 1, phase-weighted magnitude spectra of the Fourier descriptor of the silhouette contours at ten phases of a gait period are used to analyse the spatio-temporal changes of the subject’s shape. A component-based Fourier descriptor based on anatomical studies of human body is used to achieve robustness against shape variations caused by all common types of small carrying conditions with folded hands, at the subject’s back and in upright position. In phase 2, a full-body shape and motion analysis is performed by fitting ellipses to contour segments of ten phases of a gait period and using a histogram matching with Bhattacharyya distance of parameters of the ellipses as dissimilarity scores. In phase 3, dynamic time warping is used to analyse the angular rotation pattern of the subject’s leading knee with a consideration of arm-swing over a gait period to achieve identification that is invariant to walking speed, limited clothing variations, hair style changes and shadows under feet. The match scores generated in the three phases are fused using weight-based score-level fusion for robust identification in the presence of missing and distorted frames, and occlusion in the scene. Experimental analyses on various publicly available data sets show that STS-DM outperforms several state-of-the-art gait recognition methods

A Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Method

In this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCA-KNN) approach. The proposed method consists of three parts. The first part is based on preprocessing face images using Graph Based algorithm and SIFT (Scale Invariant Feature Transform) descriptor. Graph Based topology is used for matching two face images. In the second part eigen values and eigen vectors are extracted from each input face images. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. In the final part a nearest neighbor classifier is designed for classifying the face images based on the SPCA-KNN algorithm. The algorithm has been tested on 100 different subjects (15 images for each class). The experimental result shows that the proposed method has a positive effect on overall face recognition performance and outperforms other examined methods