The design of a face recognition system ( FRS ) can been separated into two major modules ¡V face detection and face recognition. In the face detection part, we combine image pre-processing techniques with maximum-likelihood estimation to detect the nearest frontal face in a single image. Under limited restrictions, our detection method overcomes some of the challenging tasks, such as variability in scale, location, orientation, facial expression, occlusion ( glasses ), and lighting change. In the face recognition part, we use both Karhunen-Loeve transform and linear discrimant analysis ( LDA ) to perform feature extraction. In this feature extraction process, the features are calculated from the inner products of the original samples and the selected eigenvectors. In general, as the size of the face database is increased, the recognition time will be proportionally increased. To solve this problem, hard-limited Karhunen-Loeve transform ( HLKLT ) is applied to reduce the computation time in our FRS.

Structured representation of input physical patterns as a set of local features has been useful for a veriety of robotics and human computer interaction (HCI) applications. It enables a stable understanding of the variable inputs. However, this representation does not fit the conventional machine learning algorithms and distance metrics because they assume vector inputs. To learn from input patterns with variable structure is thus challenging. To address this problem, I propose a general and systematic method to design distance metrics between structured inputs that can be used in conventional learning algorithms. Based on the observation of the stability in the geometric distributions of local features over the physical patterns across similar inputs, this is done combining the local similarities and the conformity of the geometric relationship between local features. The produced distance metrics, called “parametric kernels”, are positive semi-definite and require almost linear time to compute. To demonstrate the general applicability and the efficacy of this approach, I designed and applied parametric kernels to handwritten character recognition, on-line face recognition, and object detection from laser range finder sensor data. Parametric kernels achieve recognition rates competitive to state-of-the-art approaches in these tasks. / text

In this Master’s degree work artificial neural networks and back propagation learning algorithm for human faces and pattern recognition are analyzed. In the second part of work artificial neural networks and their architecture and structures models are analyzed. In the third part of article the backpropagation procedure and procedures theoretical learning principle are analyzed. In the fourth part different kinds of ANN methods and patterns extracting methods in recognition, learning and classification use were researched. In this part RGB method for patterns features extraction was described. In the fifth part the requirements specification, prototype model, use case diagram, system architecture, programs modules and objects project for software realization were created. In the same part backpropagation procedures running principle was realized. After the project part was completed, a face and patterns recognition system was created. In the sixth part the created software system was tested. According to the testing results software’s recognition rate is 82,5 % using supervised learning and 82,8 % using unsupervised learning. We found using the FAR and FRR rates the ERR rate, which was 40 %. While doing the testing with changed human characteristics, the system showed 84,6 % recognition rate. This rate shows very good work of the system by a little bit changed human characteristics. Systems realization was evaluated by users as very good one. In the seventh part software’s... [to full text]

Face detection and recognition has been very popular topics. Recently, its applications for mobile devices have gained tremendous attention due to the rapid expansion of the market. Although numerous techniques exist for face detection and recognition, only a few solve realistic challenges under the mobile device application environment. In this thesis, we propose an automatic face authentication system including both face detection and recognition components for mobile device applications by using spatially enhanced Local Binary Patterns (LBP) feature extraction. The first contribution is to propose a fast and accurate face detector by using LBP features and its spatially enhanced variant. The simplicity of LBP ensures low computational complexity and spatially enhanced LBP achieves high accuracy. The second contribution is to propose color based spatially enhanced LBP features for face recognition. The proposed features achieve high accuracy by extracting complementary information from color channels and spatial correlations between LBP features.

This Ph.D thesis work is dedicated to 3D facial surface analysis, processing as well as to the newly proposed 3D face recognition modality, which is based on mapping techniques. Facial surface processing and analysis is one of the most important steps for 3Dface recognition algorithms. Automatic anthropometric facial features localization also plays an important role for face localization, face expression recognition, face registration ect., thus its automatic localization is a crucial step for 3D face processing algorithms. In this work we focused on precise and rotation invariant landmarks localization, which are later used directly for face recognition. The landmarks are localized combining local surface properties expressed in terms of differential geometry tools and global facial generic model, used for face validation. Since curvatures, which are differential geometry properties, are sensitive to surface noise, one of the main contributions of this thesis is a modification of curvatures calculation method. The modification incorporates the surface noise into the calculation method and helps to control smoothness of the curvatures. Therefore the main facial points can be reliably and precisely localized (100% nose tip localization using 8 mm precision)under the influence of rotations and surface noise. The modification of the curvatures calculation method was also tested under different face model resolutions, resulting in stable curvature values. Finally, since curvatures analysis leads to many facial landmark candidates, the validation of which is time consuming, facial landmarks localization based on learning technique was proposed. The learning technique helps to reject incorrect landmark candidates with a high probability, thus accelerating landmarks localization. Face recognition using 3D models is a relatively new subject, which has been proposed to overcome shortcomings of 2D face recognition modality. However, 3Dface recognition algorithms are likely more complicated. Additionally, since 3D face models describe facial surface geometry, they are more sensitive to facial expression changes. Our contribution is reducing dimensionality of the input data by mapping3D facial models on to 2D domain using non-rigid, conformal mapping techniques. Having 2D images which represent facial models, all previously developed 2D face recognition algorithms can be used. In our work, conformal shape images of 3Dfacial surfaces were fed in to 2D2 PCA, achieving more than 86% recognition rate rank-one using the FRGC data set. The effectiveness of all the methods has been evaluated using the FRGC and Bosphorus datasets.

Face detection and recognition has been very popular topics. Recently, its applications for mobile devices have gained tremendous attention due to the rapid expansion of the market. Although numerous techniques exist for face detection and recognition, only a few solve realistic challenges under the mobile device application environment. In this thesis, we propose an automatic face authentication system including both face detection and recognition components for mobile device applications by using spatially enhanced Local Binary Patterns (LBP) feature extraction. The first contribution is to propose a fast and accurate face detector by using LBP features and its spatially enhanced variant. The simplicity of LBP ensures low computational complexity and spatially enhanced LBP achieves high accuracy. The second contribution is to propose color based spatially enhanced LBP features for face recognition. The proposed features achieve high accuracy by extracting complementary information from color channels and spatial correlations between LBP features.

This thesis presents a technique for face recognition that is based on image registration. The image registration technique is based on finding a set of feature points in the two images and using these feature points for registration. This is done in four steps. In the first, images are filtered with the Mexican hat wavelet to obtain the feature point locations. In the second, the Zernike moments of neighbourhoods around the feature points are calculated and compared in the third step to establish correspondence between feature points in the two images and in the fourth the transformation parameters between images are obtained using an iterative weighted least squares technique. The face recognition technique consists of three parts, a training part, an image registration part and a post-processing part. During training a set of images are chosen as the training images and the Zernike moments for the feature points of the training images are obtained and stored. In the registration part, the transformation parameters to register the training images with the images under consideration are obtained. In the post-processing, these transformation parameters are used to determine whether a valid match is found or not. The performance of the proposed method is evaluated using various face databases and it is compared with the performance of existing techniques. Results indicate that the proposed technique gives excellent results for face recognition in conditions of varying pose, illumination, background and scale. These results are comparable to other well known face recognition techniques.

A face authentication system based on principal component analysis and neural networks is developed in this thesis. The system consists of three stages / preprocessing, principal component analysis, and recognition. In preprocessing stage, normalization illumination, and head orientation were done. Principal component analysis is applied to find the aspects of face which are important for identification. Eigenvectors and eigenfaces are calculated from the initial face image set. New faces are projected onto the space expanded by eigenfaces and represented by weighted sum of the eigenfaces. These weights are used to identify the faces. Neural network is used to create the face database and recognize and authenticate the face by using these weights. In this work, a separate network was build for each person. The input face is projected onto the eigenface space first and new descriptor is obtained. The new descriptor is used as input to each person&amp / #8217 / s network, trained earlier. The one with maximum output is selected and reported as the host if it passes predefined recognition threshold. The algorithms that have been developed are tested on ORL, Yale and Feret Face Databases.

Different approaches to the face recognition are studied in this thesis. These approaches are PCA (Eigenface), Kernel Eigenface and Fisher LDA. Principal component analysis extracts the most important information contained in the face to construct a computational model that best describes the face. In Eigenface approach, variation between the face images are described by using a set of characteristic face images in order to find out the eigenvectors (Eigenfaces) of the covariance matrix of the distribution, spanned by a training set of face images. Then, every face image is represented by a linear combination of these eigenvectors. Recognition is implemented by projecting a new image into the face subspace spanned by the Eigenfaces and then classifying the face by comparing its position in face space with the positions of known individuals. In Kernel Eigenface method, non-linear mapping of input space is implemented before PCA in order to handle non-linearly embedded properties of images (i.e. background differences, illumination changes, and facial expressions etc.). In Fisher LDA, LDA is applied after PCA to increase the discrimination between classes. These methods are implemented on three databases that are: Yale face database, AT&amp / T (formerly Olivetti Research Laboratory) face database, and METU Vision Lab face database. Experiment results are compared with respect to the effects of changes in illumination, pose and expression. Kernel Eigenface and Fisher LDA show slightly better performance with respect to Eigenfaces method under changes in illumination. Expression differences did not affect the performance of Eigenfaces method. From test results, it can be observed that Eigenfaces approach is an adequate method that can be used in face recognition systems due to its simplicity, speed and learning capability. By this way, it can easily be used in real time systems.

Thesis (M.Comp.Sc.)--University of Wollongong, 2008. / Typescript. Includes bibliographical references: leaf 88-98.