Facial Asymmetry Analysis Based on 3-D Dynamic Scans

Facial dysfunction is a fundamental symptom which often relates to many neurological illnesses, such as stroke, Bell’s palsy, Parkinson’s disease, etc. The current methods for detecting and assessing facial dysfunctions mainly rely on the trained practitioners which have significant limitations as they are often subjective. This paper presents a computer-based methodology of facial asymmetry analysis which aims for automatically detecting facial dysfunctions. The method is based on dynamic 3-D scans of human faces. The preliminary evaluation results testing on facial sequences from Hi4D-ADSIP database suggest that the proposed method is able to assist in the quantification and diagnosis of facial dysfunctions for neurological patients

A statistical shape model for deformable surface

This short paper presents a deformable surface registration scheme which is based on the statistical shape modelling technique. The method consists of two major processing stages, model building and model fitting. A statistical shape model is first built using a set of training data. Then the model is deformed and matched to the new data by a modified iterative closest point (ICP) registration process. The proposed method is tested on real 3-D facial data from BU-3DFE database. It is shown that proposed method can achieve a reasonable result on surface registration, and can be used for patient position monitoring in radiation therapy and potentially can be used for monitoring of the radiation therapy progress for head and neck patients by analysis of facial articulation

Towards a comprehensive 3D dynamic facial expression database

Human faces play an important role in everyday life, including the expression of person identity, emotion and intentionality, along with a range of biological functions. The human face has also become the subject of considerable research effort, and there has been a shift towards understanding it using stimuli of increasingly more realistic formats. In the current work, we outline progress made in the production of a database of facial expressions in arguably the most realistic format, 3D dynamic. A suitable architecture for capturing such 3D dynamic image sequences is described and then used to record seven expressions (fear, disgust, anger, happiness, surprise, sadness and pain) by 10 actors at 3 levels of intensity (mild, normal and extreme). We also present details of a psychological experiment that was used to formally evaluate the accuracy of the expressions in a 2D dynamic format. The result is an initial, validated database for researchers and practitioners. The goal is to scale up the work with more actors and expression types

3-D Shape Matching for Face Analysis and Recognition

The aims of this paper are to introduce a 3-D shape matching scheme for automatic face recognition and to demonstrate its invariance to pose and facial expressions. The core of this scheme lies on the combination of non-rigid deformation registration and statistical shape modelling. While the former matches 3-D faces regardless of facial expression variations, the latter provides a low-dimensional feature vector that describes the deformation after the shape matching process, thereby enabling robust identification of 3-D faces. In order to assist establishment of accurate dense point correspondences, an isometric embedding shape representation is introduced, which is able to transform 3-D faces to a canonical form that retains the intrinsic geometric structure and achieve shape alignment of 3-D faces independent from individual’s facial expression. The feasibility and effectiveness of the proposed method was investigated using standard publicly available Gavab and BU-3DFE databases, which contain faces expressions and pose variations. The performance of the system was compared with the existing benchmark approaches and it demonstrates that the proposed scheme provides a competitive solution for the face recognition task with real-world practicality

3-D facial expression representation using B-spline statistical shape model

Effective representation and recognition of human faces are essential in a number of applications including human-computer interaction (HCI), bio-metrics or video conferencing. This paper presents initial results obtained for a novel method of 3-D facial expressions representation based on the shape space vector of the statistical shape model. The statistical shape model is constructed based on the control points of the B-spline surfaces of the train-ing data set. The model fitting for the data is achieved by a modified iterative closest point (ICP) method with the surface deformations restricted to the es-timated shape space. The proposed method is fully automated and tested on the synthetic 3-D facial data with various facial expressions. Experimental results show that the proposed 3-D facial expression representation can be potentially used for practical applications

3-D Face Recognition Using Geodesic-Map Representation and Statistical Shape Modelling

3-D face recognition research has received significant attention in the past two decades because of the rapid development in imaging technology and ever increasing security demand of modern society. One of its challenges is to cope with non-rigid deformation among faces, which is often caused by the changes of appearance and facial expression. Popular solutions to deal with this problem are to detect the deformable parts of the face and exclude them, or to represent a face in terms of sparse signature points, curves or patterns that are invariant to deformation. Such approaches, however, may lead to loss of information which is important for classification. In this paper, we propose a new geodesic-map representation with statistical shape modelling for handling the non-rigid deformation challenge in face recognition. The proposed representation captures all geometrical information from the entire 3-D face and provides a compact and expression-free map that preserves intrinsic geometrical information. As a result, the search for dense points correspondence in the face recognition task can be speeded up by using a simple image-based method instead of time-consuming, recursive closest distance search in 3-D space. An experimental investigation was conducted on 3-D face scans using publicly available databases and compared with the benchmark approaches. The experimental results demonstrate that the proposed scheme provides a highly competitive new solution for 3-D face recognition

Low dimensional Surface Parameterisation with application in biometrics

This paper describes initial results from a novel low dimensional surface parameterisation approach based on a modified iterative closest point (ICP) registration process which uses vertex based principal component analysis (PCA) to incorporate a deformable element into registration process. Using this method a 3D surface is represented by a shape space vector of much smaller dimensionality than the dimensionality of the original data space vector. The proposed method is tested on both simulated 3D faces with different facial expressions and real face data. It is shown that the proposed surface representation can be potentially used as feature space for a facial expression recognition system

3-D facial expression representation using statistical shape models

This poster describes a methodology for facial expressions representation, using 3-D/4-D data, based on the statistical shape modelling technology. The proposed method uses a shape space vector to model surface deformations, and a modified iterative closest point (ICP) method to calculate the point correspondence between each surface. The shape space vector is constructed using principal component analysis (PCA) computed for typical surfaces represented in a training data set. It is shown that the calculated shape space vector can be used as a significant feature for subsequent facial expression classification. Comprehensive 3-D/4-D face data sets have been used for building the deformation models and for testing, which include 3-D synthetic data generated from FaceGen Modeller® software, 3-D facial expression data caputed by a static 3-D scanner in the BU-3DFE database and 3-D video sequences captured at the ADSIP research centre using a 3dMD® dynamic 3-D scanner

Minimisation of energy consumption variance for multi-process manufacturing lines through genetic algorithm manipulation of production schedule

Typical manufacturing scheduling algorithms do not consider the energy consumption of each job, or its variance, when they generate a production schedule. This can become problematic for manufacturers when local infrastructure has limited energy distribution capabilities. In this paper, a genetic algorithm based schedule modification algorithm is presented. By referencing energy consumption models for each job, adjustments are made to the original schedule so that it produces a minimal variance in the total energy consumption in a multi-process manufacturing production line, all while operating within the constraints of the manufacturing line and individual processes. Empirical results show a significant reduction in energy consumption variance can be achieved on schedules containing multiple concurrent jobs