Ioana Bacivarov

Separating Directional Lighting Variability in Statistical Face Modelling Based on Texture Space Decomposition

In this paper we propose a simple method for decomposing the linear texture space of a facial appearance model into two linear subspaces, one for inter-individual variability and another for variations caused by directional changes of the lighting conditions. The approach used is to create one linear subspace from individuals with uniform illumination conditions and then filter a set of images with various directional lighting conditions by projecting corresponding textures on the previously built space; the residues are further used to build a second subspace for directional lighting. The resulted subspaces are orthogonal, so the overall texture model can be obtained by a simple concatenation of the two subspaces. The main advantage of this representation is that two sets of parameters are used to control inter-individual variation and separately intra-individual variation due to changes in illumination conditions.

Statistical models of appearance for eye tracking and eye-blink detection and measurement

A statistical active appearance model (AAM) is developed to track and detect eye blinking. The model has been designed to be robust to variations of head pose or gaze. In particular we analyze and determine the model parameters which encode the variations caused by blinking. This global model is further extended using a series of sub-models to enable independent modeling and tracking of the two eye regions. Several methods to enable measurement and detection of eye-blink are proposed and evaluated. The results of various tests on different image databases are presented to validate each model.

Next Generation Face Tracking Technology Using AAM Techniques

This paper realizes an investigation of the active appearance model (AAM) techniques, with particular emphasis on problems related to the implementation of AAM in realtime face tracking applications for embedded systems. The paper includes (a) new training and model construction approaches for AAMs with increased robustness to head pose variations and changes in the illumination conditions, and (b) a corresponding AAM-based face tracking scheme.

Smart cameras: 2D affine models for determining subject facial expressions

In this paper we consider how next generation of smart cameras will move beyond face detection and tracking to develop more useful analysis tools for determining the expressions and emotions of the subjects in images. An initial proof-of-concept using an extension of an Active Appearance Model (AAM) to measure facial parameters and a set of classifiers to determine facial states are described. Preliminary findings are encouraging and suggest that the use of the component-based AAM models and SVM classifiers will enable new applications and opportunities in smart cameras.

Facial expression modeling using component AAM models — Gaming applications

In this paper we provide preliminary results on a new modeling approach for improved determination of facial expressions from a low-resolution video stream. An initial proof-of-concept using an extension of an Active Appearance Model (AAM) to measure facial parameters and a set of classifiers to determine facial states are described. We also discuss the potential for applying this technique to determine the emotional state of the players of a computer game and suggest how this information can be integrated into the workflow of a game. In addition we describe a number of use cases where the game environment can be adapted based on feedback from the players.

A Statistical Modeling based System for Blink Detection in Digital Cameras

In this paper we describe a system which uses statistical eye modeling to track the eyes and detect eyes blinking. The main challenge was to design an eye model robust to small variations of head pose and to extract the parameters of the model which encode the variation caused by blinking.

Smart cameras: 2D affine models for robust person recognition in consumer images

In this paper we consider how next generation smart cameras will move beyond face detection and tracking to develop more useful analysis tools for determining the expression and emotions of the subjects in images. An initial proof-of-concept using an Active Appearance Model (AAM) to perform face recognition which is robust to pose and illumination variations is presented. Our initial results are very encouraging and suggest that the incorporation of a specialized AAM within next generation digital cameras will enable more accurate management and sorting of personal image collections based on the actual persons in the images.