Classification based on symmetric maximized minimal distance in subspace (SMMS)

We introduce a new classification algorithm based on the concept of symmetric maximized minimal distance in subspace (SMMS). Given the training data of authentic samples and imposter samples in the feature space, SMMS tries to identify a subspace in which all the authentic samples are close to each other and all the imposter samples are far away from the authentic samples. The optimality of the subspace is determined by maximizing the minimal distance between the authentic samples and the imposter samples in the subspace. We present a procedure to achieve such optimality and to identify the decision boundary. The verification procedure is simple since we only need to project the test sample to the subspace and compare it against the decision boundary. Using face authentication as an example, we show that the proposed algorithm outperforms several other algorithms based on support vector machines (SVM).