Two heads are better than one: Dual systems obtain better performance in facial comparison

Forensic facial image comparison based on recognition algorithms has been widely applied in forensic science. Previous researches have been concentrating on the cases of using single system during comparison, while how to use multiple systems has not yet been studied. In this paper, a dual-systems model (including SeetaFace and FaceNet) for facial comparison was constructed, and Bayesian networks were utilized as the basic frame. In order to prove its superiority, a large-scale experiment (on the dataset CelebA) has been carried on to evaluate the score-based likelihood ratio. We used three likelihood ratio evaluation tools (Empirical Cross-Entropy, Cost Likelihood Ratio, Limit Tippett Plots) to assess the performance of the model. The Wasserstein distance was also used to evaluate the detailed likelihood ratio performance. The experimental results show that the likelihood ratio performance of our dual-systems model is better than single system. Besides, our method of model building and evaluation can also be used in the condition of triple or more systems.

Automated summary

Forensic facial image comparison based on recognition algorithms has been widely used in forensic science. This paper proposes a dual-systems model and utilizes Bayesian networks as the basic framework. Experimental results show that the likelihood ratio performance of the dual-systems model is better than a single system, and the method of model building and evaluation can also be applied to multiple systems.