Various Approaches of Support vector Machines and combined Classifiers in Face Recognition

In this paper we present the various approaches used in face recognition from 2001-2012.because in last decade face recognition is using in many fields like Security sectors, identity authentication. Today we need correct and speedy performance in face recognition. This time the face recognition technology is in matured stage because research is conducting continuously in this field. Some extensions of Support vector machine (SVM) is reviewed that gives amazing performance in face recognition.Here we also review some papers of combined classifier approaches that is also a dynamic research area in a pattern recognition

A unified framework for subspace based face recognition.

Wang Xiaogang.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 88-91).Abstracts in English and Chinese.Abstract --- p.iAcknowledgments --- p.vTable of Contents --- p.viList of Figures --- p.viiiList of Tables --- p.xChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Face recognition --- p.1Chapter 1.2 --- Subspace based face recognition technique --- p.2Chapter 1.3 --- Unified framework for subspace based face recognition --- p.4Chapter 1.4 --- Discriminant analysis in dual intrapersonal subspaces --- p.5Chapter 1.5 --- Face sketch recognition and hallucination --- p.6Chapter 1.6 --- Organization of this thesis --- p.7Chapter Chapter 2 --- Review of Subspace Methods --- p.8Chapter 2.1 --- PCA --- p.8Chapter 2.2 --- LDA --- p.9Chapter 2.3 --- Bayesian algorithm --- p.12Chapter Chapter 3 --- A Unified Framework --- p.14Chapter 3.1 --- PCA eigenspace --- p.16Chapter 3.2 --- Intrapersonal and extrapersonal subspaces --- p.17Chapter 3.3 --- LDA subspace --- p.18Chapter 3.4 --- Comparison of the three subspaces --- p.19Chapter 3.5 --- L-ary versus binary classification --- p.22Chapter 3.6 --- Unified subspace analysis --- p.23Chapter 3.7 --- Discussion --- p.26Chapter Chapter 4 --- Experiments on Unified Subspace Analysis --- p.28Chapter 4.1 --- Experiments on FERET database --- p.28Chapter 4.1.1 --- PCA Experiment --- p.28Chapter 4.1.2 --- Bayesian experiment --- p.29Chapter 4.1.3 --- Bayesian analysis in reduced PCA subspace --- p.30Chapter 4.1.4 --- Extract discriminant features from intrapersonal subspace --- p.33Chapter 4.1.5 --- Subspace analysis using different training sets --- p.34Chapter 4.2 --- Experiments on the AR face database --- p.36Chapter 4.2.1 --- "Experiments on PCA, LDA and Bayes" --- p.37Chapter 4.2.2 --- Evaluate the Bayesian algorithm for different transformation --- p.38Chapter Chapter 5 --- Discriminant Analysis in Dual Subspaces --- p.41Chapter 5.1 --- Review of LDA in the null space of and direct LDA --- p.42Chapter 5.1.1 --- LDA in the null space of --- p.42Chapter 5.1.2 --- Direct LDA --- p.43Chapter 5.1.3 --- Discussion --- p.44Chapter 5.2 --- Discriminant analysis in dual intrapersonal subspaces --- p.45Chapter 5.3 --- Experiment --- p.50Chapter 5.3.1 --- Experiment on FERET face database --- p.50Chapter 5.3.2 --- Experiment on the XM2VTS database --- p.53Chapter Chapter 6 --- Eigentransformation: Subspace Transform --- p.54Chapter 6.1 --- Face sketch recognition --- p.54Chapter 6.1.1 --- Eigentransformation --- p.56Chapter 6.1.2 --- Sketch synthesis --- p.59Chapter 6.1.3 --- Face sketch recognition --- p.61Chapter 6.1.4 --- Experiment --- p.63Chapter 6.2 --- Face hallucination --- p.69Chapter 6.2.1 --- Multiresolution analysis --- p.71Chapter 6.2.2 --- Eigentransformation for hallucination --- p.72Chapter 6.2.3 --- Discussion --- p.75Chapter 6.2.4 --- Experiment --- p.77Chapter 6.3 --- Discussion --- p.83Chapter Chapter 7 --- Conclusion --- p.85Publication List of This Thesis --- p.87Bibliography --- p.8

RECOGNITION OF FACES FROM SINGLE AND MULTI-VIEW VIDEOS

Face recognition has been an active research field for decades. In recent years, with videos playing an increasingly important role in our everyday life, video-based face recognition has begun to attract considerable research interest. This leads to a wide range of potential application areas, including TV/movies search and parsing, video surveillance, access control etc. Preliminary research results in this field have suggested that by exploiting the abundant spatial-temporal information contained in videos, we can greatly improve the accuracy and robustness of a visual recognition system. On the other hand, as this research area is still in its infancy, developing an end-to-end face processing pipeline that can robustly detect, track and recognize faces remains a challenging task. The goal of this dissertation is to study some of the related problems under different settings. We address the video-based face association problem, in which one attempts to extract face tracks of multiple subjects while maintaining label consistency. Traditional tracking algorithms have difficulty in handling this task, especially when challenging nuisance factors like motion blur, low resolution or significant camera motions are present. We demonstrate that contextual features, in addition to face appearance itself, play an important role in this case. We propose principled methods to combine multiple features using Conditional Random Fields and Max-Margin Markov networks to infer labels for the detected faces. Different from many existing approaches, our algorithms work in online mode and hence have a wider range of applications. We address issues such as parameter learning, inference and handling false positves/negatives that arise in the proposed approach. Finally, we evaluate our approach on several public databases. We next propose a novel video-based face recognition framework. We address the problem from two different aspects: To handle pose variations, we learn a Structural-SVM based detector which can simultaneously localize the face fiducial points and estimate the face pose. By adopting a different optimization criterion from existing algorithms, we are able to improve localization accuracy. To model other face variations, we use intra-personal/extra-personal dictionaries. The intra-personal/extra-personal modeling of human faces has been shown to work successfully in the Bayesian face recognition framework. It has additional advantages in scalability and generalization, which are of critical importance to real-world applications. Combining intra-personal/extra-personal models with dictionary learning enables us to achieve state-of-arts performance on unconstrained video data, even when the training data come from a different database. Finally, we present an approach for video-based face recognition using camera networks. The focus is on handling pose variations by applying the strength of the multi-view camera network. However, rather than taking the typical approach of modeling these variations, which eventually requires explicit knowledge about pose parameters, we rely on a pose-robust feature that eliminates the needs for pose estimation. The pose-robust feature is developed using the Spherical Harmonic (SH) representation theory. It is extracted using the surface texture map of a spherical model which approximates the subject's head. Feature vectors extracted from a video are modeled as an ensemble of instances of a probability distribution in the Reduced Kernel Hilbert Space (RKHS). The ensemble similarity measure in RKHS improves both robustness and accuracy of the recognition system. The proposed approach outperforms traditional algorithms on a multi-view video database collected using a camera network

Unifying the Visible and Passive Infrared Bands: Homogeneous and Heterogeneous Multi-Spectral Face Recognition

Face biometrics leverages tools and technology in order to automate the identification of individuals. In most cases, biometric face recognition (FR) can be used for forensic purposes, but there remains the issue related to the integration of technology into the legal system of the court. The biggest challenge with the acceptance of the face as a modality used in court is the reliability of such systems under varying pose, illumination and expression, which has been an active and widely explored area of research over the last few decades (e.g. same-spectrum or homogeneous matching). The heterogeneous FR problem, which deals with matching face images from different sensors, should be examined for the benefit of military and law enforcement applications as well. In this work we are concerned primarily with visible band images (380-750 nm) and the infrared (IR) spectrum, which has become an area of growing interest.;For homogeneous FR systems, we formulate and develop an efficient, semi-automated, direct matching-based FR framework, that is designed to operate efficiently when face data is captured using either visible or passive IR sensors. Thus, it can be applied in both daytime and nighttime environments. First, input face images are geometrically normalized using our pre-processing pipeline prior to feature-extraction. Then, face-based features including wrinkles, veins, as well as edges of facial characteristics, are detected and extracted for each operational band (visible, MWIR, and LWIR). Finally, global and local face-based matching is applied, before fusion is performed at the score level. Although this proposed matcher performs well when same-spectrum FR is performed, regardless of spectrum, a challenge exists when cross-spectral FR matching is performed. The second framework is for the heterogeneous FR problem, and deals with the issue of bridging the gap across the visible and passive infrared (MWIR and LWIR) spectrums. Specifically, we investigate the benefits and limitations of using synthesized visible face images from thermal and vice versa, in cross-spectral face recognition systems when utilizing canonical correlation analysis (CCA) and locally linear embedding (LLE), a manifold learning technique for dimensionality reduction. Finally, by conducting an extensive experimental study we establish that the combination of the proposed synthesis and demographic filtering scheme increases system performance in terms of rank-1 identification rate

Recognizing complex faces and gaits via novel probabilistic models

In the field of computer vision, developing automated systems to recognize people under unconstrained scenarios is a partially solved problem. In unconstrained sce- narios a number of common variations and complexities such as occlusion, illumi- nation, cluttered background and so on impose vast uncertainty to the recognition process. Among the various biometrics that have been emerging recently, this dissertation focus on two of them namely face and gait recognition. Firstly we address the problem of recognizing faces with major occlusions amidst other variations such as pose, scale, expression and illumination using a novel PRObabilistic Component based Interpretation Model (PROCIM) inspired by key psychophysical principles that are closely related to reasoning under uncertainty. The model basically employs Bayesian Networks to establish, learn, interpret and exploit intrinsic similarity mappings from the face domain. Then, by incorporating e cient inference strategies, robust decisions are made for successfully recognizing faces under uncertainty. PROCIM reports improved recognition rates over recent approaches. Secondly we address the newly upcoming gait recognition problem and show that PROCIM can be easily adapted to the gait domain as well. We scienti cally de ne and formulate sub-gaits and propose a novel modular training scheme to e ciently learn subtle sub-gait characteristics from the gait domain. Our results show that the proposed model is robust to several uncertainties and yields sig- ni cant recognition performance. Apart from PROCIM, nally we show how a simple component based gait reasoning can be coherently modeled using the re- cently prominent Markov Logic Networks (MLNs) by intuitively fusing imaging, logic and graphs. We have discovered that face and gait domains exhibit interesting similarity map- pings between object entities and their components. We have proposed intuitive probabilistic methods to model these mappings to perform recognition under vari- ous uncertainty elements. Extensive experimental validations justi es the robust- ness of the proposed methods over the state-of-the-art techniques.

Towards building a practical face recognition system

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 56-57).by Wasiuddin A.S.M. Wahid.M.Eng

A Robust Face Recognition Algorithm for Real-World Applications

The proposed face recognition algorithm utilizes representation of local facial regions with the DCT. The local representation provides robustness against appearance variations in local regions caused by partial face occlusion or facial expression, whereas utilizing the frequency information provides robustness against changes in illumination. The algorithm also bypasses the facial feature localization step and formulates face alignment as an optimization problem in the classification stage

Organising a photograph collection based on human appearance

This thesis describes a complete framework for organising digital photographs in an unsupervised manner, based on the appearance of people captured in the photographs. Organising a collection of photographs manually, especially providing the identities of people captured in photographs, is a time consuming task. Unsupervised grouping of images containing similar persons makes annotating names easier (as a group of images can be named at once) and enables quick search based on query by example. The full process of unsupervised clustering is discussed in this thesis. Methods for locating facial components are discussed and a technique based on colour image segmentation is proposed and tested. Additionally a method based on the Principal Component Analysis template is tested, too. These provide eye locations required for acquiring a normalised facial image. This image is then preprocessed by a histogram equalisation and feathering, and the features of MPEG-7 face recognition descriptor are extracted. A distance measure proposed in the MPEG-7 standard is used as a similarity measure. Three approaches to grouping that use only face recognition features for clustering are analysed. These are modified k-means, single-link and a method based on a nearest neighbour classifier. The nearest neighbour-based technique is chosen for further experiments with fusing information from several sources. These sources are context-based such as events (party, trip, holidays), the ownership of photographs, and content-based such as information about the colour and texture of the bodies of humans appearing in photographs. Two techniques are proposed for fusing event and ownership (user) information with the face recognition features: a Transferable Belief Model (TBM) and three level clustering. The three level clustering is carried out at “event” level, “user” level and “collection” level. The latter technique proves to be most efficient. For combining body information with the face recognition features, three probabilistic fusion methods are tested. These are the average sum, the generalised product and the maximum rule. Combinations are tested within events and within user collections. This work concludes with a brief discussion on extraction of key images for a representation of each cluster

Face recognition in controlled environments using multiple images

Our objective is the design of a face recognition system for its use in controlled environments. For that reason, different tasks will be performed, that chronologically can be stated as: The study of the basis of pattern recognition, and especially the statistical pattern recognition. The study of the main feature selection techniques, focusing in principal component analysis (PCA), and the basic classifiers, as the nearest neighbour and Parzen classifiers. The study of the main combinations techniques, as we will try to take advantage of the availability of sets of faces to make better-founded decisions The evaluation of the main problems, like pose variation or light changes. The choice and posterior adaptation of these different general tools to our needs, like the use of modifications of the Parzen classifier. The design of the main algorithm, focusing on the differences between the two working modes, identifying (when we try to recognize a person among the different people in our Data collection Cut out faces Normaliza-tionFeature selection PCA / LDA projectionModel selection Represen-tation of the classes UpdatingClassifier selection k-NN ParzenTraining Parameter optimiza-tion Testing Identifica-tion / verifica-tion of groups of faces 14 system) and verifying (when we know a priori the id of the person and we try to assure its identity), and the use of single images or sets of faces. The implementation of the algorithms in C, trying to become familiar with the development environment and considering the possible optimization but maintaining the clarity of the code. The testing of the whole application using faces from different databases and from real data; that is, captured in real scenarios and in non optimal condition