This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed

Face Description with Local Binary Patterns: Application to Face Recognition

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

/pdf/computer-vision-algorithms-and-applications-25dn6wu83j.pdf

Computer Vision: Algorithms and Applications

A morphable model for the synthesis of 3D faces

Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve for correspondences epsivnerally a computationally expensive task that becomes impractical for large set sizes. We present a new fast kernel function which maps unordered feature sets to multi-resolution histograms and computes a weighted histogram intersection in this space. This "pyramid match" computation is linear in the number of features, and it implicitly finds correspondences based on the finest resolution histogram cell where a matched pair first appears. Since the kernel does not penalize the presence of extra features, it is robust to clutter. We show the kernel function is positive-definite, making it valid for use in learning algorithms whose optimal solutions are guaranteed only for Mercer kernels. We demonstrate our algorithm on object recognition tasks and show it to be accurate and dramatically faster than current approaches

/pdf/the-pyramid-match-kernel-discriminative-classification-with-3cuqkgrrl6.pdf

The pyramid match kernel: discriminative classification with sets of image features

Gaussian mixture models (GMMs) have proven extremely successful for text-independent speaker recognition. The standard training method for GMM models is to use MAP adaptation of the means of the mixture components based on speech from a target speaker. Recent methods in compensation for speaker and channel variability have proposed the idea of stacking the means of the GMM model to form a GMM mean supervector. We examine the idea of using the GMM supervector in a support vector machine (SVM) classifier. We propose two new SVM kernels based on distance metrics between GMM models. We show that these SVM kernels produce excellent classification accuracy in a NIST speaker recognition evaluation task.

Support vector machines using GMM supervectors for speaker verification

Over the last years significant efforts have been made to develop kernels that can be applied to sequence data such as DNA, text, speech, video and images. The Fisher Kernel and similar variants have been suggested as good ways to combine an underlying generative model in the feature space and discriminant classifiers such as SVM's. In this paper we suggest an alternative procedure to the Fisher kernel for systematically finding kernel functions that naturally handle variable length sequence data in multimedia domains. In particular for domains such as speech and images we explore the use of kernel functions that take full advantage of well known probabilistic models such as Gaussian Mixtures and single full covariance Gaussian models. We derive a kernel distance based on the Kullback-Leibler (KL) divergence between generative models. In effect our approach combines the best of both generative and discriminative methods and replaces the standard SVM kernels. We perform experiments on speaker identification/verification and image classification tasks and show that these new kernels have the best performance in speaker verification and mostly outperform the Fisher kernel based SVM's and the generative classifiers in speaker identification and image classification.

/pdf/a-kullback-leibler-divergence-based-kernel-for-svm-2eypu28bpw.pdf

A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications

http://cbcl.mit.edu/publications/ps/heisele-cviu-03.pdf

Face recognition: component-based versus global approaches

An improved authentication system utilizes multi-factor user authentication. In an exemplary embodiment, one authentication factor is the user's speech pattern, and another authentication factor is a one-time passcode. The speech pattern and the passcode may be provided via voice portal and/or browser input. The speech pattern is routed to a speaker verification subsystem, while the passcode is routed to a passcode validation subsystem. Many other combinations of input types are also possible. For heightened security, the two (or more) authentication factors are preferably, although not necessarily, provided over differing communication channels (i.e., they are out-of-band with respect to each other). If a user is authenticated by the multi-factor process, he is given access to one or more desired secured applications. Policy and authentication procedures may be abstracted from the applications to allow a single sign-on across multiple applications.

Robust multi-factor authentication for secure application environments

The recognition accuracy of current discriminant architectures for visual recognition is hampered by the dependence on holistic image representations, where images are represented as vectors in a high-dimensional space Such representations lead to complex classification problems due to the need to 1) restrict image resolution and 2) model complex manifolds due to variations in pose, lighting, and other imaging variables Localized representations, where images are represented as bags of low-dimensional vectors, are significantly less affected by these problems but have traditionally been difficult to combine with discriminant classifiers such as the support vector machine (SVM) This limitation has recently been lifted by the introduction of probabilistic SVM kernels, such as the Kullback-Leibler (KL) kernel In this work we investigate the advantages of using this kernel as a means to combine discriminant recognition with localized representations We derive a taxonomy of kernels based on the combination of the KL-kernel with various probabilistic representation previously proposed in the recognition literature Experimental evaluation shows that these kernels can significantly outperform traditional SVM solutions for recognition

/pdf/the-kullback-leibler-kernel-as-a-framework-for-discriminant-4bkp117mzw.pdf

The Kullback-Leibler kernel as a framework for discriminant and localized representations for visual recognition

support vector machine, SVM, speaker identification, speaker verification, KL divergence, Kullback-Leibler divergence, probabilistic distance kernels, multimedia One major SVM weakness has been the use of generic kernel functions to compute distances among data points. Polynomial, linear, and Gaussian are typical examples. They do not take full advantage of the inherent probability distributions of the data. Focusing on audio speaker identification and verification, we propose to explore the use of novel kernel functions that take full advantage of good probabilistic and descriptive models of audio data. We explore the use of generative speaker identification models such as Gaussian Mixture Models and derive a kernel distance based on the Kullback-Leibler (KL) divergence between generative models. In effect our approach combines the best of both generative and discriminative methods. Our results show that these new kernels perform as well as baseline GMM classifiers and outperform generic kernel based SVM’s in both speaker identification and verification on two different audio databases.

http://www.hpl.hp.com/techreports/2004/HPL-2004-7.pdf

Purdy Ho

Papers

A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications

Face recognition: component-based versus global approaches

Robust multi-factor authentication for secure application environments

The Kullback-Leibler kernel as a framework for discriminant and localized representations for visual recognition

A New SVM Approach to Speaker Identification and Verification Using Probabilistic Distance Kernels