Discriminant Learning Using Training Space Partitioning
Marios Kyperoundtas, Marios Kyperountas, Anastasios Tefas, Anastasios Tefas, Ioannis Pitas · IGI Global eBooks · 2010
Large training databases introduce a level of complexity that often degrades the classification performance of face recognition methods. In this chapter, an overview of various approaches that are employed in order to overcome this problem is presented and, in addition, a specific discriminant learning approach that combines dynamic training and partitioning is described in detail. This face recognition methodology employs dynamic training in order to implement a person-specific iterative classification process. This process employs discriminant clustering, where, by making use of an entropy-based measure, the algorithm adapts the coordinates of the discriminant space with respect to the characteristics of the test face. As a result, the training space is dynamically reduced to smaller spaces, where linear separability among the face classes is more likely to be achieved. The process iterates until one final cluster is retained, which consists of a single face class that represents the best match to the test face. The performance of this methodology is evaluated on standard large face databases and results show that the proposed framework gives a good solution to the face recognition problem.