Complementary Discriminant Analysis for Classification of Double Attributes

Real-world objects often have two or more significant attributes. For example, face images have attributes of persons, expressions, and so on. Even if we are interested in only one of those attributes, additional informations on auxiliary attributes can help recognition of the main one. In the present paper, the authors propose a method for pattern recognition with double attributes. A pair of classifiers are combined: each classifier makes a guess of its corresponding attribute, and it tells the guess to the other as a hint. Equilibrium point of this iteration can be calculated directly without iterative procedures.