24 research outputs found
Shape classification: towards a mathematical description of the face
Recent advances in biostereometric techniques have led to the quick and easy
acquisition of 3D data for facial and other biological surfaces. This has led facial
surgeons to express dissatisfaction with landmark-based methods for analysing the
shape of the face which use only a small part of the data available, and to seek a method
for analysing the face which maximizes the use of this extensive data set. Scientists
working in the field of computer vision have developed a variety of methods for the
analysis and description of 2D and 3D shape. These methods are reviewed and an
approach, based on differential geometry, is selected for the description of facial shape.
For each data point, the Gaussian and mean curvatures of the surface are calculated.
The performance of three algorithms for computing these curvatures are evaluated for
mathematically generated standard 3D objects and for 3D data obtained from an optical
surface scanner. Using the signs of these curvatures, the face is classified into eight
'fundamental surface types' - each of which has an intuitive perceptual meaning. The
robustness of the resulting surface type description to errors in the data is determined
together with its repeatability.
Three methods for comparing two surface type descriptions are presented and illustrated
for average male and average female faces. Thus a quantitative description of facial
change, or differences between individual's faces, is achieved. The possible application
of artificial intelligence techniques to automate this comparison is discussed. The
sensitivity of the description to global and local changes to the data, made by
mathematical functions, is investigated.
Examples are given of the application of this method for describing facial changes
made by facial reconstructive surgery and implications for defining a basis for facial
aesthetics using shape are discussed. It is also applied to investigate the role played by
the shape of the surface in facial recognition
On the Recognition of Emotion from Physiological Data
This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create âweakly induced emotionsâ. Recordings of the participantsâ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure