3D Face Recognition using Significant Point based SULD Descriptor

In this work, we present a new 3D face recognition method based on Speeded-Up Local Descriptor (SULD) of significant points extracted from the range images of faces. The proposed model consists of a method for extracting distinctive invariant features from range images of faces that can be used to perform reliable matching between different poses of range images of faces. For a given 3D face scan, range images are computed and the potential interest points are identified by searching at all scales. Based on the stability of the interest point, significant points are extracted. For each significant point we compute the SULD descriptor which consists of vector made of values from the convolved Haar wavelet responses located on concentric circles centred on the significant point, and where the amount of Gaussian smoothing is proportional to the radii of the circles. Experimental results show that the newly proposed method provides higher recognition rate compared to other existing contemporary models developed for 3D face recognition

Face Image Recognition Using 2D PCA Algorithm

The global features of face image have been extensively used for face recognition however they are sensitive to variations caused by expressions, illumination, pose, occlusions and makeup. The paper describes the enhancement in the behavior of the 2D PCA (Principles Component Analysis) based recognition algorithm that recognize face images by adding noise removal filter before and after the recognition stage, PCA algorithm based on information theory concept, seeks a computational model that best describes a face by extracting the most relevant information contained, and compare the eigenface with the eigenfaces in the gallery database, the euclidean distance check the face image acceptance with noise removal filter added as an additional step to modify the performance of classic PCA algorithm to get better recognition

Feature extraction for range image interpretation using local topology statistics

This thesis presents an approach for interpreting range images of known subject matter, such as the human face, based on the extraction and matching of local features from the images. In recent years, approaches to interpret two-dimensional (2D) images based on local feature extraction have advanced greatly, for example, systems such as Scale Invariant Feature Transform (SIFT) can detect and describe the local features in the 2D images effectively. With the aid of rapidly advancing three-dimensional (3D) imaging technology, in particular, the advent of commercially available surface scanning systems based on photogrammetry, image representation has been able to extend into the third dimension. Moreover, range images confer a number of advantages over conventional 2D images, for instance, the properties of being invariant to lighting, pose and viewpoint changes. As a result, an attempt has been made in this work to establish how best to represent the local range surface with a feature descriptor, thereby developing a matching system that takes advantages of the third dimension present in the range images and casting this in the framework of an existing scale and rotational invariance recognition technology: SIFT. By exploring the statistical representations of the local variation, it is possible to represent and match range images of human faces. This can be achieved by extracting unique mathematical keys known as feature descriptors, from the various automatically generated stable keypoint locations of the range images, thereby capturing the local information of the distributions of the mixes of surface types and their orientations simultaneously. Keypoints are generated through scale-space approach, where the (x,y) location and the appropriate scale (sigma) are detected. In order to achieve invariance to in-plane viewpoint rotational changes, a consistent canonical orientation is assigned to each keypoint and the sampling patch is rotated to this canonical orientation. The mixes of surface types, derived using the shape index, and the image gradient orientations are extracted from each sampling patch by placing nine overlapping Gaussian sub-regions over the measurement aperture. Each of the nine regions is overlapped by one standard deviation in order to minimise the occurrence of spatial aliasing during the sampling stages and to provide a better continuity within the descriptor. Moreover, surface normals can be computed from each of the keypoint location, allowing the local 3D pose to be estimated and corrected within the feature descriptors since the orientations in which the images were captured are unknown a priori. As a result, the formulated feature descriptors have strong discriminative power and are stable to rotational changes

A Few Days of A Robot's Life in the Human's World: Toward Incremental Individual Recognition

PhD thesisThis thesis presents an integrated framework and implementation for Mertz, an expressive robotic creature for exploring the task of face recognition through natural interaction in an incremental and unsupervised fashion. The goal of this thesis is to advance toward a framework which would allow robots to incrementally ``get to know'' a set of familiar individuals in a natural and extendable way. This thesis is motivated by the increasingly popular goal of integrating robots in the home. In order to be effective in human-centric tasks, the robots must be able to not only recognize each family member, but also to learn about the roles of various people in the household.In this thesis, we focus on two particular limitations of the current technology. Firstly, most of face recognition research concentrate on the supervised classification problem. Currently, one of the biggest problems in face recognition is how to generalize the system to be able to recognize new test data that vary from the training data. Thus, until this problem is solved completely, the existing supervised approaches may require multiple manual introduction and labelling sessions to include training data with enough variations. Secondly, there is typically a large gap between research prototypes and commercial products, largely due to lack of robustness and scalability to different environmental settings.In this thesis, we propose an unsupervised approach which wouldallow for a more adaptive system which can incrementally update thetraining set with more recent data or new individuals over time.Moreover, it gives the robots a more natural {\em socialrecognition} mechanism to learn not only to recognize each person'sappearance, but also to remember some relevant contextualinformation that the robot observed during previous interactionsessions. Therefore, this thesis focuses on integrating anunsupervised and incremental face recognition system within aphysical robot which interfaces directly with humans through naturalsocial interaction. The robot autonomously detects, tracks, andsegments face images during these interactions and automaticallygenerates a training set for its face recognition system. Moreover,in order to motivate robust solutions and address scalabilityissues, we chose to put the robot, Mertz, in unstructured publicenvironments to interact with naive passersby, instead of with onlythe researchers within the laboratory environment.While an unsupervised and incremental face recognition system is acrucial element toward our target goal, it is only a part of thestory. A face recognition system typically receives eitherpre-recorded face images or a streaming video from a static camera.As illustrated an ACLU review of a commercial face recognitioninstallation, a security application which interfaces with thelatter is already very challenging. In this case, our target goalis a robot that can recognize people in a home setting. Theinterface between robots and humans is even more dynamic. Both therobots and the humans move around.We present the robot implementation and its unsupervised incremental face recognition framework. We describe analgorithm for clustering local features extracted from a large set of automatically generated face data. We demonstrate the robot's capabilities and limitations in a series of experiments at a public lobby. In a final experiment, the robot interacted with a few hundred individuals in an eight day period and generated a training set of over a hundred thousand face images. We evaluate the clustering algorithm performance across a range of parameters on this automatically generated training data and also the Honda-UCSD video face database. Lastly, we present some recognition results using the self-labelled clusters

R.A.: Face Recognition Using 2D and 3D Multimodal Local Features

Abstract. Machine recognition of faces is very challenging because it is an interclass recognition problem and the variation in faces is very low compared to other biometrics. Global features have been extensively used for face recognition however they are sensitive to variations caused by expressions, illumination, pose, occlusions and makeup. We present a novel 3D local feature for automatic face recognition which is robust to these variations. The 3D features are extracted by uniformly sampling local regions of the face in locally defined coordinate bases which makes them invariant to pose. The high descriptiveness of this feature makes it ideal for the challenging task of interclass recognition. In the 2D domain, we use the SIFT descriptor and fuse the results with the 3D approach at the score level. Experiments were performed using the FRGC v2.0 data and the achieved verification rates at 0.001 FAR were 98.5 % and 86.0% for faces with neutral and non-neutral expressions respectively.