3:30, MULT-P1.1
HYBRID INDEPENDENT COMPONENT ANALYSIS AND SUPPORT VECTOR MACHINE LEARNING SCHEME FOR FACE DETECTION
Y. QI, D. DOERMANN, D. DEMENTHON
In this paper we propose a new hybrid unsupervised / supervised learning scheme that integrates Independent Component Analysis (ICA) with the Support Vector Machine (SVM) approach and apply this new learning scheme to the face detection problem. In low-level feature extraction, ICA produces independent image bases that emphasize edge information in the image data. In high-level classification, SVM classifies the ICA features as a face or non-face. Our experimental results show that by using ICA features we obtained a larger margin of separation and fewer support vectors than by training SVM directly on the image data. This indicates better generalization performance, which was verified in our experiments.

3:30, MULT-P1.2
NOSE SHAPE ESTIMATION AND TRACKING FOR MODEL-BASED CODING
A. BASU, L. YIN
Detecting and tracking the nose shape is non-trivial, and plays an equally important role as eyes and mouth for model based coding, especially for analysis and synthesis of realistic facial expressions. In this paper, a feature detection method on the facial organ areas is presented. Individual templates are designed for the nostril and nose-side. First, the feature regions are limited to certain areas by using two-stage region growing methods. Second, the pre-defined templates are applied to extract the shape of the nostril and nose-side. Finally, the extracted feature shapes are exploited to guide a facial model to complete an accurate adaptation. The advantage of the proposed scheme is demonstrated by experiments on real video sequences for low bit rate video coding. (Demonstration sequences can be seen at www.cs.ualberta.ca/~anup/MPEG4/demo.html.)

3:30, MULT-P1.3
A NEW OPTIMIZATION PROCEDURE FOR EXTRACTING THE POINT-BASED LIP CONTOUR USING ACTIVE SHAPE MODEL
K. SUM, S. LEUNG, A. LIEW, K. TSE, W. LAU
This paper presents a new optimization procedure for extracting the point-based lip contour using Active Shape Model. A 14-point ASM lip model is used to describe the lip contour. With the aid of fuzzy clustering analysis, a probability map of a RGB lip image is obtained and a region-based cost function is established. The new optimization procedure operates on the spatial domain (actual contour points) and all the points are pulled towards their desirable locations in each iteration. Hence, the lip contour evolution becomes better controlled and consequently fast convergence is achieved. The new procedure can also achieve real-time performance on lip contour extraction and tracking from lip image sequence.

3:30, MULT-P1.4
RECOGNITION OF FACE PROFILES FROM THE MUGSHOT DATABASE USING A HYBRID CONNECTIONIST/HMM APPROACH
F. WALLHOFF, S. MÜLLER, G. RIGOLL
Biometrical systems have been the focus of concentrated research efforts in recent years. Face recognition technology has reached a level of performance at which frontal-view recognition of faces with slightly different facial expressions, view angles or head poses can be considered nearly solved. In this paper we present a novel hybrid ANN/HMM approach to recognize a person from that person's profile view although the recognition system is trained with only one single frontal view of the person. Such a system can be useful for mugshot identification where a victim or witness has seen the criminal from the side only. Our approach uses neural methods in order to synthesize a profile out of the frontal view using no additional knowledge about the 3D shape and structure of a human head. The classification of the generated images is accomplished using a statistical HMM-approach.

3:30, MULT-P1.5
PRINCIPAL COMPONENT ANALYSIS FOR FACIAL ANIMATION
K. GOUDEAUX, T. CHEN, S. WANG, J. LIU
This paper presents a technique for animating a three-dimensional face model through the application of Principal Component Analysis (PCA). Using PCA has several advantages over traditional approaches to facial animation because it reduces the number of parameters needed to describe a face and confines the facial motion to a valid space to prevent unnatural contortions. First real data is optically captured in real time from a human subject using infrared cameras and reflective trackers. This data is analyzed to find a mean face and a set of eigenvectors and eigenvalues that are used to perturb the mean face within the range described by the captured data. The result is a set of vectors that can be linearly combined and interpolated to represent different facial expressions and animations.

3:30, MULT-P1.6
CONTENT-BASED INDEXING OF IMAGES AND VIDEO USING FACE DETECTION AND RECOGNITION METHODS
S. EICKELER, F. WALLHOFF, U. IURGEL, G. RIGOLL
This paper presents an image and video indexing approach that combines face detection and face recognition methods. The images of a database or frames of a video sequence are scanned for faces by a Neural Network-based face detector. The extracted faces are then grouped into clusters by a combination of a face recognition method using pseudo two-dimensional Hidden Markov Models and the k-means clustering algorithm. Each resulting main cluster consists of the face images of one person. In a subsequent step the detected faces are labeled as one of the different people in the video sequence or the image database and the occurrence of the people can be evaluated. The results of the proposed approach on a TV broadcast news sequence are presented. The system was able to distinguish between three different newscasters and an interviewed person.

3:30, MULT-P1.7
A REAL-TIME FACE TRACKER FOR COLOR VIDEO
S. SPORS, R. RABENSTEIN
This paper presents a face localization and tracking algorithm which is based upon skin color detection and principle component analysis (PCA) based eye localization. Skin color segmentation is performed using statistical models for human skin color. The skin color segmentation task results in a mask marking the skin color regions in the actual frame, which is further used to compute the position and size of the dominant facial region utilizing a robust statistics-based localization method. To improve the results of skin color segmentation a foreground/background segmentation and an adaptive background update scheme were added. Additionally the derived face position is tracked with an Kalman filter. To overcome the problem of skin color ambiguity an eye detection algorithm based upon the principle component analysis (PCA) is presented.

3:30, MULT-P1.8
CREATING 3-D VIRTUAL HEADS FROM VIDEO SEQUENCE: A RECURSIVE APPROACH COMBINING EKF AND DFFD
K. CHOI, J. HWANG, Y. LUO
An automatic system for creating a virtual head that is compatible with MPEG-4 facial object specification is presented. Color classification and a valley detection filter are performed to find face and Facial Definition Points (FDPs) at the initialization stage. Extracted FDPs are tracked by normalized correlation and their trajectories are fed into an extended Kalman filter (EKF) to recover camera geometry, facial orientation, and depth of selected FDPs. Based on a recovered point-wise 3D structure, Dirichlet Free-Form Deformations (DFFD) is applied to deform a generic 3D model. Once a virtual head is created, the head can be used to track FDPs for large out-of-plane rotations and to update the head model again based on refined depth information. A complete texture map is created by mixing frontal and rotated faces based on the recovered face orientation.