Robustness of the New Owner-Tester Approach for Face Identification Experiments

With the broad application of face identification, it is important that the performance estimated for an algorithm using a sample can be generalized to the population performance. We proposed using an Owner-Tester setup to replace the current approach for experiments on face identification (or other biometrics and pattern recognition systems). This paper looks into the robustness of the Owner-Tester setup in terms of goodness of fit and performance estimation using misidentification risk -the newly suggested performance evaluation metric. Testing results have indicated that the approach is robust in term of goodness of fit and performance estimation.