Registration of Face Image Using Modified BRISK Feature Descriptor

Automatic face recognition is a hot area of research in the field of computer vision. Even though a lot of research have been done in this field, still researchers are unable to develop an algorithm which can detect the face images under all possible real time conditions. Automatic face recognition algorithms are used in a variety of applications such as surveillance, automatic tagging, and human-robot interaction etc. The main problem faced by researchers working with the above real time problems is the uncertainty about the pose of the detected face, i.e. if the pose of the sensed image differ from the images in the trained database most of the existing algorithms will fail. So researchers suggested and proved that the detection accuracy against pose variation can be improved if we considered image registration as a preprocessing step prior to face recognition. In this work, scale and rotation invariant features have been used for image registration. The important steps in feature based image registration are preprocessing, feature detection, feature matching, transformation estimation, and resampling. In this work, feature detectors and descriptors like SIFT, SURF, FAST, DAISY and BRISK are used. Among all these descriptors the BRISK descriptor performs the best. To avoid mismatches, using some threshold values, a modified BRISK descriptor has been proposed in this work. Modified BRISK descriptor performs best in terms of maximum matching as compared to other state of arts descriptors. The next step is to calculate the transformation model which is capable of transforming the coordinates of sensed image to coordinates of reference image. Some radial basis functions are used in this step to design the proper transformation function. In resampling step, we used bilinear interpolation to compute some pixels in the output image. A new algorithm is proposed in this work to find out the possible image pairs from the train database corresponds to the input image, for doing image registration. In this work, image registration algorithms are simulated in MATLAB with different detector-descriptor combination and affine transformation matrix. For measuring the similarity between registered output image and the reference image, SSIM index and mutual information is used

Robust Image Registration with Global Intensity Transformation

This paper presents a registration method for images with global illumination variations. The method is based on a joint iterative optimization (geometric and photometric) of the L1 norm of the intensity error. Two strategies are compared to directly find the appropriate intensity transformation within each iteration: histogram specification and the solution obtained by analyzing the necessary optimality conditions. Such strategies reduce the search space of the joint optimization to that of the geometric transformation between the images

Robust signatures for 3D face registration and recognition

PhDBiometric authentication through face recognition has been an active area of research for the last few decades, motivated by its application-driven demand. The popularity of face recognition, compared to other biometric methods, is largely due to its minimum requirement of subject co-operation, relative ease of data capture and similarity to the natural way humans distinguish each other. 3D face recognition has recently received particular interest since three-dimensional face scans eliminate or reduce important limitations of 2D face images, such as illumination changes and pose variations. In fact, three-dimensional face scans are usually captured by scanners through the use of a constant structured-light source, making them invariant to environmental changes in illumination. Moreover, a single 3D scan also captures the entire face structure and allows for accurate pose normalisation. However, one of the biggest challenges that still remain in three-dimensional face scans is the sensitivity to large local deformations due to, for example, facial expressions. Due to the nature of the data, deformations bring about large changes in the 3D geometry of the scan. In addition to this, 3D scans are also characterised by noise and artefacts such as spikes and holes, which are uncommon with 2D images and requires a pre-processing stage that is speci c to the scanner used to capture the data. The aim of this thesis is to devise a face signature that is compact in size and overcomes the above mentioned limitations. We investigate the use of facial regions and landmarks towards a robust and compact face signature, and we study, implement and validate a region-based and a landmark-based face signature. Combinations of regions and landmarks are evaluated for their robustness to pose and expressions, while the matching scheme is evaluated for its robustness to noise and data artefacts

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions