Compact image representation by binary component analysis

We propose binary component analysis (BCA) for compact image representation and apply it to fractional dimension reduction and Binaryface representation. BCA is similar to the widely used principal component analysis (PCA), but with the restriction of the base vectors taking binary values of +1 and −1, instead of real values. In a finite set of all binary vectors, the projection of the correlated data onto the binary components (BC) have the largest possible variances. BCA leads to fractional dimension reduction, where binary components successively reduce data variance. The top few BC's capture the largest variances, and each additional BC further reduces the variance by a fraction of the amount reduced in one dimension by PCA. BCA is applied to compact face representation as Binaryface and used for face classification.