We propose an optimization framework for nonconvex problems based on majorization-minimization that is particularity well-suited for parallel computing. It reduces the optimization of a high dimensional nonconvex objective function to successive optimizations of locally tight and convex upper bounds which are additively separable into low dimensional objectives. The original problem is then broken into simpler and parallel tasks, while guaranteeing the monotonic reduction of the original objective function and convergence to a local minimum. This framework also allows one to restrict the upper bound to a local dynamic convex domain, so that the bound is better matched to the local curvature of the objective function, resulting in accelerated convergence. We test the proposed framework on a nonconvex support vector machine based on a sigmoid loss function and on nonconvex penalized logistic regression.

The electrical signal originating from the heart, the electrocardiogram (ECG), has been examined for its potential use as a biometric. Recent ECG studies have shown that an intersession authentication performance$\sim 2$% EER. Moreover, in the identification mode, with a testing database containing 200 individuals, the rank-1 accuracy improves from$\sim 80$% for each individual biometric system, to$\sim 92$% for the multibiometric system. Although there are implementation issues that would need to be resolved before this combined method could be applied in the field, this report establishes the basis and utility of the method in principle, and it identifies effective signal analysis approaches.

The electrocardiogram (ECG) is an emerging biometric modality that has seen about 13 years of development in peer-reviewed literature, and as such deserves a systematic review and discussion of the associated methods and findings. In this paper, we review most of the techniques that have been applied to the use of the electrocardiogram for biometric recognition. In particular, we categorize the methodologies based on the features and the classification schemes. Finally, a comparative analysis of the authentication performance of a few of the ECG biometric systems is presented, using our inhouse database. The comparative study includes the cases where training and testing data come from the same and different sessions (days). The authentication results show that most of the algorithms that have been proposed for ECG-based biometrics perform well when the training and testing data come from the same session. However, when training and testing data come from different sessions, a performance degradation occurs. Multiple training sessions were incorporated to diminish the loss in performance. That notwithstanding, only a few of the proposed ECG recognition algorithms appear to be able to support performance improvement due to multiple training sessions. Only three of these algorithms produced equal error rates (EERs) in the single digits, including an EER of 5.5% using a method proposed by us.