3,117 research outputs found
Analysis of 3D Face Reconstruction
This thesis investigates the long standing problem of 3D reconstruction from a single 2D face
image. Face reconstruction from a single 2D face image is an ill posed problem involving estimation of the intrinsic and the extrinsic camera parameters, light parameters, shape parameters
and the texture parameters. The proposed approach has many potential applications in the
law enforcement, surveillance, medicine, computer games and the entertainment industries.
This problem is addressed using an analysis by synthesis framework by reconstructing a 3D
face model from identity photographs. The identity photographs are a widely used medium for
face identi cation and can be found on identity cards and passports.
The novel contribution of this thesis is a new technique for creating 3D face models from a single
2D face image. The proposed method uses the improved dense 3D correspondence obtained
using rigid and non-rigid registration techniques. The existing reconstruction methods use the
optical
ow method for establishing 3D correspondence. The resulting 3D face database is used
to create a statistical shape model.
The existing reconstruction algorithms recover shape by optimizing over all the parameters
simultaneously. The proposed algorithm simplifies the reconstruction problem by using a step
wise approach thus reducing the dimension of the parameter space and simplifying the opti-
mization problem. In the alignment step, a generic 3D face is aligned with the given 2D face
image by using anatomical landmarks. The texture is then warped onto the 3D model by using
the spatial alignment obtained previously. The 3D shape is then recovered by optimizing over
the shape parameters while matching a texture mapped model to the target image.
There are a number of advantages of this approach. Firstly, it simpli es the optimization requirements and makes the optimization more robust. Second, there is no need to accurately
recover the illumination parameters. Thirdly, there is no need for recovering the texture parameters by using a texture synthesis approach. Fourthly, quantitative analysis is used for
improving the quality of reconstruction by improving the cost function. Previous methods use
qualitative methods such as visual analysis, and face recognition rates for evaluating reconstruction accuracy.
The improvement in the performance of the cost function occurs as a result of improvement
in the feature space comprising the landmark and intensity features. Previously, the feature
space has not been evaluated with respect to reconstruction accuracy thus leading to inaccurate
assumptions about its behaviour.
The proposed approach simpli es the reconstruction problem by using only identity images,
rather than placing eff ort on overcoming the pose, illumination and expression (PIE) variations.
This makes sense, as frontal face images under standard illumination conditions are widely
available and could be utilized for accurate reconstruction. The reconstructed 3D models with
texture can then be used for overcoming the PIE variations
Biometric Systems
Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study
DeepNav: Joint View Learning for Direct Optimal Path Perception in Cochlear Surgical Platform Navigation
Although much research has been conducted in the field of automated cochlear implant navigation, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as identifying the optimal navigation zone (OPZ) in the cochlear. In this paper, a 2.5D joint-view convolutional neural network (2.5D CNN) is proposed and evaluated for the identification of the OPZ in the cochlear segments. The proposed network consists of 2 complementary sagittal and bird-view (or top view) networks for the 3D OPZ recognition, each utilizing a ResNet-8 architecture consisting of 5 convolutional layers with rectified nonlinearity unit (ReLU) activations, followed by average pooling with size equal to the size of the final feature maps. The last fully connected layer of each network has 4 indicators, equivalent to the classes considered: the distance to the adjacent left and right walls, collision probability and heading angle. To demonstrate this, the 2.5D CNN was trained using a parametric data generation model, and then evaluated using anatomically constructed cochlea models from the micro-CT images of different cases. Prediction of the indicators demonstrates the effectiveness of the 2.5D CNN, for example the heading angle has less than 1° error with computation delays of less that <1 milliseconds
Biometric fusion methods for adaptive face recognition in computer vision
PhD ThesisFace recognition is a biometric method that uses different techniques to identify the individuals based on the facial information received from digital image data. The system of face recognition is widely used for security purposes, which has challenging problems. The solutions to some of the most important challenges are proposed in this study. The aim of this thesis is to investigate face recognition across pose problem based on the image parameters of camera calibration. In this thesis, three novel methods have been derived to address the challenges of face recognition and offer solutions to infer the camera parameters from images using a geomtric approach based on perspective projection. The following techniques were used: camera calibration CMT and Face Quadtree Decomposition (FQD), in order to develop the face camera measurement technique (FCMT) for human facial recognition.
Facial information from a feature extraction and identity-matching algorithm has been created. The success and efficacy of the proposed algorithm are analysed in terms of robustness to noise, the accuracy of distance measurement, and face recognition. To overcome the intrinsic and extrinsic parameters of camera calibration parameters, a novel technique has been developed based on perspective projection, which uses different geometrical shapes to calibrate the camera. The parameters used in novel measurement technique CMT that enables the system to infer the real distance for regular and irregular objects from the 2-D images. The proposed system of CMT feeds into FQD to measure the distance between the facial points. Quadtree decomposition enhances the representation of edges and other singularities along curves of the face, and thus improves directional features from face detection across face pose. The proposed FCMT system is the new combination of CMT and FQD to recognise the faces in the various pose.
The theoretical foundation of the proposed solutions has been thoroughly developed and discussed in detail. The results show that the proposed algorithms outperform existing algorithms in face recognition, with a 2.5% improvement in main error recognition rate compared with recent studies
- …