Linear Object Classes and Image Synthesis From a Single Example Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Action Units for Facial Expression Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expressive expression mapping with ratio images
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Manipulating Facial Appearance through Shape and Color
IEEE Computer Graphics and Applications
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Head Modeling from Pictures and Morphing in 3D with Image Metamorphosis Based on Triangulation
CAPTECH '98 Proceedings of the International Workshop on Modelling and Motion Capture Techniques for Virtual Environments
Facial beauty and fractal geometry
Facial beauty and fractal geometry
An introduction to variable and feature selection
The Journal of Machine Learning Research
Geometry-Driven Photorealistic Facial Expression Synthesis
IEEE Transactions on Visualization and Computer Graphics
Combined SVM-Based Feature Selection and Classification
Machine Learning
Facial expressional image synthesis controlled by emotional parameters
Pattern Recognition Letters
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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We introduce an efficient approach for representing a human face using a limited number of images. This compact representation allows for meaningful manipulation of the face. Principal Components Analysis (PCA) utilized in our research makes possible the separation of facial features so as to build statistical shape and texture models. Thus changing the model parameters can create images with different expressions and poses. By presenting newly created faces for reviewers' marking in terms of intensities on masculinity, friendliness and attractiveness, we analyze relations between the parameters and intensities. With feature selections, we sort those parameters by their importance in deciding the three aforesaid aspects. Thus we are able to control the models and transform a new face image to be a naturally masculine, friendly or attractive one. In the PCA-based feature space, we can successfully transfer expressions from one subject onto a novel person's face.