Online Face Recognition with Application to Proactive Augmented Reality

Recently, more and more researchers have concentrated on the research of video-based face recognition. The topic of this thesis is online face recognition with application to proactive augmented reality. We intend to solve online single-image and multiple-image face recognition problems when the influence of illumination variations is introduced. First, three machine learning approaches are utilized in single-image face recognition: PCA-based, 2DPCA-based, and SVM-based approaches. Illumination variations are big obstacles for face recognition. The next step in our approach therefore involves illumination normalization. Image preprocessing (AHE+RGIC) and invariant feature extraction (Eigenphases and LBP) methods are employed to compensate for illumination variations. Finally, in order to improve the recognition performance, we propose several novel algorithms to multiple-image face recognition which consider the multiple images as query data for subsequent classification. These algorithms are called MIK-NN, MMIK-NN and Kmeans+Muliple K-NN. In conclusion, the simulation experiment results show that the LBP+x2-based method efficiently compensates for the illumination effect and MMIK-NN considerably improves the performance of online face recognition

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

Head pose estimation and attentive behavior detection

Master'sMASTER OF ENGINEERIN

Two-Dimensional Face Recognition Algorithms in the Frequency Domain

ABSTRACT Two-Dimensional Face Recognition Algorithms in the Frequency Domain Alper Serhat Zeytunlu The importance of security, law-enforcement and identity verification has necessitated the development of automated stable, fast and highly accurate algorithms for human recognition. Face recognition is one of the most popular techniques used for these purposes. Face recognition algorithms are performed on very large size of datasets obtained under various challenging conditions. Principal component analysis (PCA) is a widely used technique for face recognition. However, it has major drawbacks of (i) losing the image details due to the transformation of two-dimensional face images into one-dimensional vectors, (ii) having a large time complexity due to the use of a large size covariance matrix and (iii) suffering from the adverse effect of intra-class pose variations resulting in reduced recognition accuracy. To overcome the problem of intra-class pose variations, Fourier magnitudes have been used for feature extraction in the PCA algorithm giving rise to the so called FM-PCA algorithm. However, the time complexity of this algorithm is even higher. On the other hand, to address the other two drawbacks of the PCA algorithm, two-dimensional PCA (2DPCA) algorithms have been proposed. This thesis is concerned with developing 2DPCA algorithms that incorporate the advantages of FM-PCA in improving the accuracy and that of 2DPCA algorithms in improving the accuracy as well as the time complexity. Towards this goal, 2DPCA algorithms, referred to as the FM-r2DPCA and FM-(2D)2PCA algorithms, that use Fourier-magnitudes rather than the raw pixel values, are first developed. Extensive simulations are conducted to demonstrate the effectiveness of using the Fourier-magnitudes in providing higher recognition accuracy over their spatial domain counterparts. Next, by taking advantage of the energy compaction property of the Fourier-magnitudes, the proposed algorithms are further developed to significantly reduce their computational complexities with little loss in the recognition accuracy. Simulation results are provided to validate this claim

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research

Homogeneous and Heterogeneous Face Recognition: Enhancing, Encoding and Matching for Practical Applications

Face Recognition is the automatic processing of face images with the purpose to recognize individuals. Recognition task becomes especially challenging in surveillance applications, where images are acquired from a long range in the presence of difficult environments. Short Wave Infrared (SWIR) is an emerging imaging modality that is able to produce clear long range images in difficult environments or during night time. Despite the benefits of the SWIR technology, matching SWIR images against a gallery of visible images presents a challenge, since the photometric properties of the images in the two spectral bands are highly distinct.;In this dissertation, we describe a cross spectral matching method that encodes magnitude and phase of multi-spectral face images filtered with a bank of Gabor filters. The magnitude of filtered images is encoded with Simplified Weber Local Descriptor (SWLD) and Local Binary Pattern (LBP) operators. The phase is encoded with Generalized Local Binary Pattern (GLBP) operator. Encoded multi-spectral images are mapped into a histogram representation and cross matched by applying symmetric Kullback-Leibler distance. Performance of the developed algorithm is demonstrated on TINDERS database that contains long range SWIR and color images acquired at a distance of 2, 50, and 106 meters.;Apart from long acquisition range, other variations and distortions such as pose variation, motion and out of focus blur, and uneven illumination may be observed in multispectral face images. Recognition performance of the face recognition matcher can be greatly affected by these distortions. It is important, therefore, to ensure that matching is performed on high quality images. Poor quality images have to be either enhanced or discarded. This dissertation addresses the problem of selecting good quality samples.;The last chapters of the dissertation suggest a number of modifications applied to the cross spectral matching algorithm for matching low resolution color images in near-real time. We show that the method that encodes the magnitude of Gabor filtered images with the SWLD operator guarantees high recognition rates. The modified method (Gabor-SWLD) is adopted in a camera network set up where cameras acquire several views of the same individual. The designed algorithm and software are fully automated and optimized to perform recognition in near-real time. We evaluate the recognition performance and the processing time of the method on a small dataset collected at WVU