The debate over the definition of beauty has been waged by both scientists and philosophers for centuries. We tested the idea that a facial configuration close to the population mean is fundamental to attractiveness.
First, we digitized images of faces of male and female college students (i.e., transformed the facial images into little dots of lightness and darkness called "pixels"). Each face is represented by a matrix of pixel values that can be mathematically averaged with the matrices of other faces. Once digitized and averaged together, we can turn the averaged pixel values back into images and have the composite faces rated for attractiveness.
College students rated the male and female composite faces as significantly higher in attractiveness than the individual faces used to create them, if the composites had at least 16 different faces in them. Thus, averaged faces are attractive. Note that when we use the word, "average," we mean the arithmetical mean, and not an average-looking person. If, for example, you take a female composite (averaged) face made of 32 different faces and overlay it on the face of an extremely attractive female model, the two images line up almost perfectly indicating that the model's facial configuration is very similar to the composites' facial configuration.
[...] we view averageness as fundamental and necessary to facial attractiveness. Averageness is not the only component of attractiveness, but without it, no face will be attractive.
Here are some selected publications on the matter.
My two cents: what makes you ugly are extreme characteristics (e.g. big nose or ears); averaging simply takes care of these 'large errors'. The same principle is behind the frequently superior performance of composite forecasts (e.g. of economic variables), where the arithmetic mean of a number of forecasts is often more accurate than any of the individual components.
Postscript: Note that averaging doesn't quite work with hair.