In this thesis a novel video telephony compression scheme is proposed, implemented and discussed. The scheme generates a talking head sequence from a head and shoulder video telephony sequence. The generated talking head mimics the facial expressions of the individual depicted in the head and shoulder input sequence. The scheme is based on model based coding and more specifically based on an ei...

The eyes are one of the most important facial features for recognizing human faces. Many face recognition systems today make use of local features (such as eyes) for identiication or veriica-tion of individuals, but no system to our knowledge has studied performance when the only available information is the eyes. In this paper we show that we can obtain 85% correct classiication on the popular...

A novel method is proposed to establish the classifier which can classify the pancreatic images into normal or abnormal. Firstly, the brightness feature is used to construct high-order tensors, then using multilinear principal component analysis (MPCA) extracts the eigentensors, and finally, the classifier is constructed based on support vector machine (SVM) and the classifier parameters are op...

A framework is presented for recovering the 3D structure and visual appearance of a human head from sparse data obtained from a real-time tracking system. An eigenvector decomposition of CyberWare-scanned heads is used to code incoming information. Modular eigenspaces are used to decorrelate eigenfeatures (eyes, nose, and mouth) from the rest of the head data. We observe that the modular eigens...

We present a nonparametric facial feature localization method using relative directional information between regularly sampled image segments and facial feature points. Instead of using any iterative parameter optimization technique or search algorithm, our method finds the location of facial feature points by using a weighted concentration of the directional vectors originating from the image ...

We compare the generalization performance of three distinct representation schemes for facial emotions using a single classification strategy (neural network). The face images presented to the classifiers are represented as: full face projections of the dataset onto their eigenvectors (eigenfaces); a similar projection constrained to eye and mouth areas (eigenfeatures); and finally a projection...

PCA is a well-know dimension reduction methodology that can also be employed for face detection task. Although the use of eigenface as the basis for face detection or recognition under PCA-based regime is so generalized, little is known about its characteristics. We study its feature by data visualization in the face space of varying dimension and the comparison of the face detection rate in di...

In this paper, we propose a novel framework to statistically measure the correlation inconsistency in mobile images for tamper detection. By first sampling a number of blocks at different image locations, we extract a set of derivative weights as features from each block using partial derivative correlation models. Through regularizing the within-image covariance eigenspectrum and performing ei...

This work proposes a method which enables us to perform kernel Fisher discriminant analysis in the whole eigenspace for face recognition. It employs the ratio of eigenvalues to decompose the entire kernel feature space into two subspaces: a reliable subspace spanned mainly by the facial variation and an unreliable subspace due to finite number of training samples. Eigenvectors are then scaled u...