An improved multi-objective optimization algorithm based on fuzzy dominance for risk minimization in biometric sensor network

Biometric system is very important for recognition in several security areas. In this paper we deal in designing biometric sensor manager by optimizing the risk. Risk is modeled as a multi-objective optimization with Global False Acceptance Rate and Global False Rejection Rate as two objectives. In practice, multiple biometric sensors are used and the decision is taken locally at each sensor and the data is passed to the sensor manager. At the sensor manager the data is fused using a fusion rule and the final decision is taken. The optimization involves designing the data fusion rule and setting the sensor thresholds. We have implemented a recent fuzzy dominance based decomposition technique for multi-objective optimization called MOEA/DFD and have compared its performance on other contemporary state-of-arts in multi-objective optimization field like MOEA/D, NSGAII. The algorithm introduces a fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method only when one of the solutions fails to dominate the other in terms of a fuzzy dominance level. We have simulated the algorithms on different number of sensor setups consisting of 3, 6, 8 sensors respectively. We have also varied the apriori probability of imposter from 0.1 to 0.9 to verify the performance of the system with varying threat. One of the most significant advantages of using multi-objective optimization is that with a single run just by changing the decision making logic applied to the obtained Pareto front one can find the required threshold and decision strategies for varying threat of imposter. But with single objective optimization one need to run the algorithms each time with change in threat of imposter. Thus multi-objective representation appears to be more useful and better than single objective one. In all the test instances MOEA/DFD performs better than all other algorithms.