Abstract: This paper addresses the challenges in face recognition system under the device-cloud collaborative structure,presenting a computation offloading strategy tailored in real-world scenarios to optimize recognition accuracy within resource constraints.Firstly,a device-cloud identification model integration method is proposed,which is different from the strict or lenient integration method,enhancing both true acceptance rate(TAR) and true rejection rate(TRR) to ensure that the collaborative accuracy of device-cloud is higher than device.Secondly,we propose a feature selection scheme based on the combination identification results,categorizing recognition risk levels by utilizing the statistical relationship between the recognition results and the proportion of positive and negative samples,and extracting feature combinations of high-risk scenarios.In addition,an optimization scheme for global resource control is proposed,which selects abnormal parks and terminals through statistical distribution differences and allocates more algorithm resources to improve global recognition accuracy.Finally,we also propose to use OC-SVM to adapt to scenarios with unequal sample distributions and outliers,facilitating the dynamic adjustment of the recall proportion.Experimental results demonstrate that the optimized scheme proposed in this paper is efficient in improving algorithmic accuracy within resource constraints,and it shows practical value and potential for application.

Key words: Face recognition, Computation offloading, Model integration