Human face recognition using a spatially weighted Hausdorff distance

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HUMAN FACE RECOGNITION BASED ON FRACTAL IMAGE CODING

Human face recognition is an important area in the field of biometrics. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. In this thesis we describe fully automatic solutions that can locate faces and then perform identification and verification. We present a solution for face localisation using eye locations. We derive an efficient representation for the decision hyperplane of linear and nonlinear Support Vector Machines (SVMs). For this we introduce the novel concept of $\rho$ and $\eta$ prototypes. The standard formulation for the decision hyperplane is reformulated and expressed in terms of the two prototypes. Different kernels are treated separately to achieve further classification efficiency and to facilitate its adaptation to operate with the fast Fourier transform to achieve fast eye detection. Using the eye locations, we extract and normalise the face for size and in-plane rotations. Our method produces a more efficient representation of the SVM decision hyperplane than the well-known reduced set methods. As a result, our eye detection subsystem is faster and more accurate. The use of fractals and fractal image coding for object recognition has been proposed and used by others. Fractal codes have been used as features for recognition, but we need to take into account the distance between codes, and to ensure the continuity of the parameters of the code. We use a method based on fractal image coding for recognition, which we call the Fractal Neighbour Distance (FND). The FND relies on the Euclidean metric and the uniqueness of the attractor of a fractal code. An advantage of using the FND over fractal codes as features is that we do not have to worry about the uniqueness of, and distance between, codes. We only require the uniqueness of the attractor, which is already an implied property of a properly generated fractal code. Similar methods to the FND have been proposed by others, but what distinguishes our work from the rest is that we investigate the FND in greater detail and use our findings to improve the recognition rate. Our investigations reveal that the FND has some inherent invariance to translation, scale, rotation and changes to illumination. These invariances are image dependent and are affected by fractal encoding parameters. The parameters that have the greatest effect on recognition accuracy are the contrast scaling factor, luminance shift factor and the type of range block partitioning. The contrast scaling factor affect the convergence and eventual convergence rate of a fractal decoding process. We propose a novel method of controlling the convergence rate by altering the contrast scaling factor in a controlled manner, which has not been possible before. This helped us improve the recognition rate because under certain conditions better results are achievable from using a slower rate of convergence. We also investigate the effects of varying the luminance shift factor, and examine three different types of range block partitioning schemes. They are Quad-tree, HV and uniform partitioning. We performed experiments using various face datasets, and the results show that our method indeed performs better than many accepted methods such as eigenfaces. The experiments also show that the FND based classifier increases the separation between classes. The standard FND is further improved by incorporating the use of localised weights. A local search algorithm is introduced to find a best matching local feature using this locally weighted FND. The scores from a set of these locally weighted FND operations are then combined to obtain a global score, which is used as a measure of the similarity between two face images. Each local FND operation possesses the distortion invariant properties described above. Combined with the search procedure, the method has the potential to be invariant to a larger class of non-linear distortions. We also present a set of locally weighted FNDs that concentrate around the upper part of the face encompassing the eyes and nose. This design was motivated by the fact that the region around the eyes has more information for discrimination. Better performance is achieved by using different sets of weights for identification and verification. For facial verification, performance is further improved by using normalised scores and client specific thresholding. In this case, our results are competitive with current state-of-the-art methods, and in some cases outperform all those to which they were compared. For facial identification, under some conditions the weighted FND performs better than the standard FND. However, the weighted FND still has its short comings when some datasets are used, where its performance is not much better than the standard FND. To alleviate this problem we introduce a voting scheme that operates with normalised versions of the weighted FND. Although there are no improvements at lower matching ranks using this method, there are significant improvements for larger matching ranks. Our methods offer advantages over some well-accepted approaches such as eigenfaces, neural networks and those that use statistical learning theory. Some of the advantages are: new faces can be enrolled without re-training involving the whole database; faces can be removed from the database without the need for re-training; there are inherent invariances to face distortions; it is relatively simple to implement; and it is not model-based so there are no model parameters that need to be tweaked

State of the Art in Face Recognition

Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state

A generic face processing framework: technologies, analyses and applications.

Jang Kim-fung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 108-124).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Introduction about Face Processing Framework --- p.4Chapter 1.2.1 --- Basic architecture --- p.4Chapter 1.2.2 --- Face detection --- p.5Chapter 1.2.3 --- Face tracking --- p.6Chapter 1.2.4 --- Face recognition --- p.6Chapter 1.3 --- The scope and contributions of the thesis --- p.7Chapter 1.4 --- The outline of the thesis --- p.8Chapter 2 --- Facial Feature Representation --- p.10Chapter 2.1 --- Facial feature analysis --- p.10Chapter 2.1.1 --- Pixel information --- p.11Chapter 2.1.2 --- Geometry information --- p.13Chapter 2.2 --- Extracting and coding of facial feature --- p.14Chapter 2.2.1 --- Face recognition --- p.15Chapter 2.2.2 --- Facial expression classification --- p.38Chapter 2.2.3 --- Other related work --- p.44Chapter 2.3 --- Discussion about facial feature --- p.48Chapter 2.3.1 --- Performance evaluation for face recognition --- p.49Chapter 2.3.2 --- Evolution of the face recognition --- p.52Chapter 2.3.3 --- Evaluation of two state-of-the-art face recog- nition methods --- p.53Chapter 2.4 --- Problem for current situation --- p.58Chapter 3 --- Face Detection Algorithms and Committee Ma- chine --- p.61Chapter 3.1 --- Introduction about face detection --- p.62Chapter 3.2 --- Face Detection Committee Machine --- p.64Chapter 3.2.1 --- Review of three approaches for committee machine --- p.65Chapter 3.2.2 --- The approach of FDCM --- p.68Chapter 3.3 --- Evaluation --- p.70Chapter 4 --- Facial Feature Localization --- p.73Chapter 4.1 --- Algorithm for gray-scale image: template match- ing and separability filter --- p.73Chapter 4.1.1 --- Position of face and eye region --- p.74Chapter 4.1.2 --- Position of irises --- p.75Chapter 4.1.3 --- Position of lip --- p.79Chapter 4.2 --- Algorithm for color image: eyemap and separa- bility filter --- p.81Chapter 4.2.1 --- Position of eye candidates --- p.81Chapter 4.2.2 --- Position of mouth candidates --- p.83Chapter 4.2.3 --- Selection of face candidates by cost function --- p.84Chapter 4.3 --- Evaluation --- p.85Chapter 4.3.1 --- Algorithm for gray-scale image --- p.86Chapter 4.3.2 --- Algorithm for color image --- p.88Chapter 5 --- Face Processing System --- p.92Chapter 5.1 --- System architecture and limitations --- p.92Chapter 5.2 --- Pre-processing module --- p.93Chapter 5.2.1 --- Ellipse color model --- p.94Chapter 5.3 --- Face detection module --- p.96Chapter 5.3.1 --- Choosing the classifier --- p.96Chapter 5.3.2 --- Verifying the candidate region --- p.97Chapter 5.4 --- Face tracking module --- p.99Chapter 5.4.1 --- Condensation algorithm --- p.99Chapter 5.4.2 --- Tracking the region using Hue color model --- p.101Chapter 5.5 --- Face recognition module --- p.102Chapter 5.5.1 --- Normalization --- p.102Chapter 5.5.2 --- Recognition --- p.103Chapter 5.6 --- Applications --- p.104Chapter 6 --- Conclusion --- p.106Bibliography --- p.10

Selected topics in video coding and computer vision

Video applications ranging from multimedia communication to computer vision have been extensively studied in the past decades. However, the emergence of new applications continues to raise questions that are only partially answered by existing techniques. This thesis studies three selected topics related to video: intra prediction in block-based video coding, pedestrian detection and tracking in infrared imagery, and multi-view video alignment.;In the state-of-art video coding standard H.264/AVC, intra prediction is defined on the hierarchical quad-tree based block partitioning structure which fails to exploit the geometric constraint of edges. We propose a geometry-adaptive block partitioning structure and a new intra prediction algorithm named geometry-adaptive intra prediction (GAIP). A new texture prediction algorithm named geometry-adaptive intra displacement prediction (GAIDP) is also developed by extending the original intra displacement prediction (IDP) algorithm with the geometry-adaptive block partitions. Simulations on various test sequences demonstrate that intra coding performance of H.264/AVC can be significantly improved by incorporating the proposed geometry adaptive algorithms.;In recent years, due to the decreasing cost of thermal sensors, pedestrian detection and tracking in infrared imagery has become a topic of interest for night vision and all weather surveillance applications. We propose a novel approach for detecting and tracking pedestrians in infrared imagery based on a layered representation of infrared images. Pedestrians are detected from the foreground layer by a Principle Component Analysis (PCA) based scheme using the appearance cue. To facilitate the task of pedestrian tracking, we formulate the problem of shot segmentation and present a graph matching-based tracking algorithm. Simulations with both OSU Infrared Image Database and WVU Infrared Video Database are reported to demonstrate the accuracy and robustness of our algorithms.;Multi-view video alignment is a process to facilitate the fusion of non-synchronized multi-view video sequences for various applications including automatic video based surveillance and video metrology. In this thesis, we propose an accurate multi-view video alignment algorithm that iteratively aligns two sequences in space and time. To achieve an accurate sub-frame temporal alignment, we generalize the existing phase-correlation algorithm to 3-D case. We also present a novel method to obtain the ground-truth of the temporal alignment by using supplementary audio signals sampled at a much higher rate. The accuracy of our algorithm is verified by simulations using real-world sequences

Face Identification by Real-Time Connectionist System

This document provides an approach to biometrics analysis which consists in the location and identification of faces in real time, making the concept a safe alternative to Web sites based on the paradigm of user and password. Numerous techniques are available to implement face recognition including the principal component analysis (PCA), neural networks, and geometric approach to the problem considering the shapes of the face representing a collection of values. The study and application of these processes originated the development of a security architecture supported by the comparison of images captured from a webcam using methodology of PCA, and the Hausdorff algorithm of distance as similarity measures between a general model of the registered user and the objects (faces) stored in the database, the result is a web authentication system with main emphasis on efficiency and application of neural networks

A Small Look at the Ear Recognition Process using a Hybrid Approach

The purpose of this document is to offer a combined approach in biometric analysis field, integrating some of the most known techniques using ears to recognize people. This study uses Hausdorff distance as a pre-processing stage adding sturdiness to increase the performance filtering for the subjects to use it in the testing process. Also includes the Image Ray Transform (IRT) and the Haar based classifier for the detection step. Then, the system computes Speeded Up Robust Features (SURF) and Linear Discriminant Analysis (LDA) as an input of two neural networks to recognize a person by the patterns of its ear. To show the applied theory experimental results, the above algorithms have been implemented using Microsoft C#. The investigation results showed robustness improving the ear recognition process

A Brief Review of the Ear Recognition Process using Deep Neural Networks

The process of precisely recognize people by ears has been getting major attention in recent years. It represents an important step in the biometric research, especially as a complement to face recognition systems which have difficult in real conditions. This is due to the great variation in shapes, variable lighting conditions, and the changing profile shape which is a planar representation of a complex object. An ear recognition system involving a convolutional neural networks (CNN) is proposed to identify a person given an input image. The proposed method matches the performance of other traditional approaches when analyzed against clean photographs. However, the F1 metric of the results shows improvements in specificity of the recognition. We also present a technique for improving the speed of a CNN applied to large input images through the optimization of the sliding window approac

The Biharmonic Eigenface

YesPrincipal component analysis (PCA) is an elegant mechanism that reduces the dimensionality of a dataset to bring out patterns of interest in it. The preprocessing of facial images for efficient face recognition is considered to be one of the epitomes among PCA applications. In this paper, we introduce a novel modification to the method of PCA whereby we propose to utilise the inherent averaging ability of the discrete Biharmonic operator as a preprocessing step. We refer to this mechanism as the BiPCA. Interestingly, by applying the Biharmonic operator to images, we can generate new images of reduced size while keeping the inherent features in them intact. The resulting images of lower dimensionality can significantly reduce the computational complexities while preserving the features of interest. Here, we have chosen the standard face recognition as an example to demonstrate the capacity of our proposed BiPCA method. Experiments were carried out on three publicly available datasets, namely the ORL, Face95 and Face96. The results we have obtained demonstrate that the BiPCA outperforms the traditional PCA. In fact, our experiments do suggest that, when it comes to face recognition, the BiPCA method has at least 25% improvement in the average percentage error rate