Face Recognition and Gender Determination

The system presented here is a specialized version of a general object recognition system. Images of faces are represented as graphs, labeled with topographical information and local templates. Different poses are represented by different graphs. New graphs of faces are generated by an elastic graph matching procedure comparing the new face with a set of precomputed graphs: the "general face knowledge". The final phase of the matching process can be used to generate composite images of faces and to determine certain features represented in the general face knowledge, such as gender or the presence of glasses or a beard. The graphs can be compared by a similarity function which makes the system efficient in recognizing faces

An Analysis of Facial Expression Recognition Techniques

In present era of technology , we need applications which could be easy to use and are user-friendly , that even people with specific disabilities use them easily. Facial Expression Recognition has vital role and challenges in communities of computer vision, pattern recognition which provide much more attention due to potential application in many areas such as human machine interaction, surveillance , robotics , driver safety, non- verbal communication, entertainment, health- care and psychology study. Facial Expression Recognition has major importance ration in face recognition for significant image applications understanding and analysis. There are many algorithms have been implemented on different static (uniform background, identical poses, similar illuminations ) and dynamic (position variation, partial occlusion orientation, varying lighting )conditions. In general way face expression recognition consist of three main steps first is face detection then feature Extraction and at last classification. In this survey paper we discussed different types of facial expression recognition techniques and various methods which is used by them and their performance measures

Mitigating the effect of covariates in face recognition

Current face recognition systems capture faces of cooperative individuals in controlled environment as part of the face recognition process. It is therefore possible to control lighting, pose, background, and quality of images. However, in a real world application, we have to deal with both ideal and imperfect data. Performance of current face recognition systems is affected for such non-ideal and challenging cases. This research focuses on designing algorithms to mitigate the effect of covariates in face recognition.;To address the challenge of facial aging, an age transformation algorithm is proposed that registers two face images and minimizes the aging variations. Unlike the conventional method, the gallery face image is transformed with respect to the probe face image and facial features are extracted from the registered gallery and probe face images. The variations due to disguises cause change in visual perception, alter actual data, make pertinent facial information disappear, mask features to varying degrees, or introduce extraneous artifacts in the face image. To recognize face images with variations due to age progression and disguises, a granular face verification approach is designed which uses dynamic feed-forward neural architecture to extract 2D log polar Gabor phase features at different granularity levels. The granular levels provide non-disjoint spatial information which is combined using the proposed likelihood ratio based Support Vector Machine match score fusion algorithm. The face verification algorithm is validated using five face databases including the Notre Dame face database, FG-Net face database and three disguise face databases.;The information in visible spectrum images is compromised due to improper illumination whereas infrared images provide invariance to illumination and expression. A multispectral face image fusion algorithm is proposed to address the variations in illumination. The Support Vector Machine based image fusion algorithm learns the properties of the multispectral face images at different resolution and granularity levels to determine optimal information and combines them to generate a fused image. Experiments on the Equinox and Notre Dame multispectral face databases show that the proposed algorithm outperforms existing algorithms. We next propose a face mosaicing algorithm to address the challenge due to pose variations. The mosaicing algorithm generates a composite face image during enrollment using the evidence provided by frontal and semiprofile face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a users face image. Experiments conducted on three different databases indicate that face mosaicing offers significant benefits by accounting for the pose variations that are commonly observed in face images.;Finally, the concept of online learning is introduced to address the problem of classifier re-training and update. A learning scheme for Support Vector Machine is designed to train the classifier in online mode. This enables the classifier to update the decision hyperplane in order to account for the newly enrolled subjects. On a heterogeneous near infrared face database, the case study using Principal Component Analysis and C2 feature algorithms shows that the proposed online classifier significantly improves the verification performance both in terms of accuracy and computational time

Face Processing & Frontal Face Verification

In this report we first review important publications in the field of face recognition; geometric features, templates, Principal Component Analysis (PCA), pseudo-2D Hidden Markov Models, Elastic Graph Matching, as well as other points are covered; important issues, such as the effects of an illumination direction change and the use of different face areas, are also covered. A new feature set (termed DCT-mod2) is then proposed; the feature set utilizes polynomial coefficients derived from 2D Discrete Cosine Transform (DCT) coefficients obtained from horizontally & vertically neighbouring blocks. Face authentication results on the VidTIMIT database suggest that the proposed feature set is superior (in terms of robustness to illumination changes and discrimination ability) to features extracted using four popular methods: PCA, PCA with histogram equalization pre-processing, 2D DCT and 2D Gabor wavelets; the results also suggest that histogram equalization pre-processing increases the error rate and offers no help against illumination changes. Moreover, the proposed feature set is over 80 times faster to compute than features based on 2D Gabor wavelets. Further experiments on the Weizmann Database also show that the proposed approach is more robust than 2D Gabor wavelets and 2D DCT coefficients

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

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

Face recognition committee machine: methodology, experiments, and a system application.

Tang Ho-Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 85-92).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Face Recognition --- p.2Chapter 1.3 --- Contributions --- p.4Chapter 1.4 --- Organization of this Thesis --- p.6Chapter 2 --- Literature Review --- p.8Chapter 2.1 --- Committee Machine --- p.8Chapter 2.1.1 --- Static Structure --- p.9Chapter 2.1.2 --- Dynamic Structure --- p.10Chapter 2.2 --- Face Recognition Algorithms Overview --- p.11Chapter 2.2.1 --- Eigenface --- p.12Chapter 2.2.2 --- Fisherface --- p.17Chapter 2.2.3 --- Elastic Graph Matching --- p.19Chapter 2.2.4 --- Support Vector Machines --- p.23Chapter 2.2.5 --- Neural Networks --- p.25Chapter 2.3 --- Commercial System and Applications --- p.27Chapter 2.3.1 --- FaceIT --- p.28Chapter 2.3.2 --- ZN-Face --- p.28Chapter 2.3.3 --- TrueFace --- p.29Chapter 2.3.4 --- Viisage --- p.30Chapter 3 --- Static Structure --- p.31Chapter 3.1 --- Introduction --- p.31Chapter 3.2 --- Architecture --- p.32Chapter 3.3 --- Result and Confidence --- p.33Chapter 3.3.1 --- "Eigenface, Fisherface, EGM" --- p.34Chapter 3.3.2 --- SVM --- p.35Chapter 3.3.3 --- Neural Networks --- p.36Chapter 3.4 --- Weight --- p.37Chapter 3.5 --- Voting Machine --- p.38Chapter 4 --- Dynamic Structure --- p.40Chapter 4.1 --- Introduction --- p.40Chapter 4.2 --- Architecture --- p.41Chapter 4.3 --- Gating Network --- p.42Chapter 4.4 --- Feedback Mechanism --- p.44Chapter 5 --- Face Recognition System --- p.46Chapter 5.1 --- Introduction --- p.46Chapter 5.2 --- System Architecture --- p.47Chapter 5.2.1 --- Face Detection Module --- p.48Chapter 5.2.2 --- Face Recognition Module --- p.49Chapter 5.3 --- Face Recognition Process --- p.50Chapter 5.3.1 --- Enrollment --- p.51Chapter 5.3.2 --- Recognition --- p.52Chapter 5.4 --- Distributed System --- p.54Chapter 5.4.1 --- Problems --- p.55Chapter 5.4.2 --- Distributed Architecture --- p.56Chapter 5.5 --- Conclusion --- p.59Chapter 6 --- Experimental Results --- p.60Chapter 6.1 --- Introduction --- p.60Chapter 6.2 --- Database --- p.61Chapter 6.2.1 --- ORL Face Database --- p.61Chapter 6.2.2 --- Yale Face Database --- p.62Chapter 6.2.3 --- AR Face Database --- p.62Chapter 6.2.4 --- HRL Face Database --- p.63Chapter 6.3 --- Experimental Details --- p.64Chapter 6.3.1 --- Pre-processing --- p.64Chapter 6.3.2 --- Cross Validation --- p.67Chapter 6.3.3 --- System details --- p.68Chapter 6.4 --- Result --- p.69Chapter 6.4.1 --- ORL Result --- p.69Chapter 6.4.2 --- Yale Result --- p.72Chapter 6.4.3 --- AR Result --- p.73Chapter 6.4.4 --- HRL Result --- p.75Chapter 6.4.5 --- Average Running Time --- p.76Chapter 6.5 --- Discussion --- p.77Chapter 6.5.1 --- Advantages --- p.78Chapter 6.5.2 --- Disadvantages --- p.79Chapter 6.6 --- Conclusion --- p.80Chapter 7 --- Conclusion --- p.82Bibliography --- p.9

Face Recognition: Study and Comparison of PCA and EBGM Algorithms

Face recognition is a complex and difficult process due to various factors such as variability of illumination, occlusion, face specific characteristics like hair, glasses, beard, etc., and other similar problems affecting computer vision problems. Using a system that offers robust and consistent results for face recognition, various applications such as identification for law enforcement, secure system access, computer human interaction, etc., can be automated successfully. Different methods exist to solve the face recognition problem. Principal component analysis, Independent component analysis, and linear discriminant analysis are few other statistical techniques that are commonly used in solving the face recognition problem. Genetic algorithm, elastic bunch graph matching, artificial neural network, etc. are few of the techniques that have been proposed and implemented. The objective of this thesis paper is to provide insight into different methods available for face recognition, and explore methods that provided an efficient and feasible solution. Factors affecting the result of face recognition and the preprocessing steps that eliminate such abnormalities are also discussed briefly. Principal Component Analysis (PCA) is the most efficient and reliable method known for at least past eight years. Elastic bunch graph matching (EBGM) technique is one of the promising techniques that we studied in this thesis work. We also found better results with EBGM method than PCA in the current thesis paper. We recommend use of a hybrid technique involving the EBGM algorithm to obtain better results. Though, the EBGM method took a long time to train and generate distance measures for the given gallery images compared to PCA. But, we obtained better cumulative match score (CMS) results for the EBGM in comparison to the PCA method. Other promising techniques that can be explored separately in other paper include Genetic algorithm based methods, Mixture of principal components, and Gabor wavelet techniques