Project - Personal Authentication Based on SMMS
As the biometric
applications become more popular, the biometric classification algorithms will
be applied to hand-held devices. Due to the size and power constraints of these
devices, the computational complexity of the verification procedure will be the
In this project, we want to study how we can build a new classifier for authentication which has the simple computational requirement on verification with the good performance. We propose a new classification algorithm based on the concept of Symmetric Maximized Minimal distance in Subspace (SMMS).
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 clustered together 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 subspace and the decision boundary. Once the subspace is trained, the verification procedure is simple since we only need to project the test sample onto the subspace and compare it against the decision boundary.
Using face authentication as an example, we show
that the proposed algorithm outperforms the linear classifier based on Support-Vector
Machines (SVM). The proposed algorithm also applies to multimodal feature
spaces. The features can come from any modalities, such as face images,
voices, fingerprints, etc.
Any suggestions or comments are welcome. Please send them to Wende Zhang.