REPRESENTATION BOUND FOR HUMAN FACIAL MIMIC WITH THE AID OF PRINCIPAL COMPONENT ANALYSIS
Ulrik Söderström, Haibo Li · International Journal of Image and Graphics · 2010
In this paper, we examine how much information is needed to represent the facial mimic, based on Paul Ekman's assumption that the facial mimic can be represented with a few basic emotions. Principal component analysis is used to compact the important facial expressions. Theoretical bounds for facial mimic representation are presented both for using a certain number of principal components and a certain number of bits. When 10 principal components are used to reconstruct color image video at a resolution of 240 × 176 pixels the representation bound is on average 36.8 dB, measured in peak signal-to-noise ratio. Practical confirmation of the theoretical bounds is demonstrated. Quantization of projection coefficients affects the representation, but a quantization with approximately 7-8 bits is found to match an exact representation, measured in mean square error.