EFaR 2023: Efficient Face Recognition Competition

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well.

Unified Identification and Tuning Approach Using Deep Neural Networks For Visual Servoing Applications

Vision based control of Unmanned Aerial Vehicles (UAVs) has been adopted by a wide range of applications due to the availability of low-cost on-board sensors and computers. Tuning such systems to work properly requires extensive domain specific experience which limits the growth of emerging applications. Moreover, obtaining performance limits of UAV based visual servoing with the current state-of-the-art is not possible due to the complexity of the models used. In this paper, we present a systematic approach for real-time identification and tuning of visual servoing systems based on a novel robustified version of the recent deep neural networks with the modified relay feedback test (DNN-MRFT) approach. The proposed robust DNN-MRFT algorithm can be used with a multitude of vision sensors and estimation algorithms despite the high levels of sensor's noise. Sensitivity of MRFT to perturbations is investigated and its effect on identification and tuning performance is analyzed. DNN-MRFT was able to detect performance changes due to the use of slower vision sensors, or due to the integration of accelerometer measurements. Experimental identification results were closely matching simulation results, which can be used to explain system behaviour and anticipate the closed loop performance limits given a certain hardware and software setup. Finally, we demonstrate the capability of the DNN-MRFT tuned visual servoing systems to reject external disturbances. Some advantages of the suggested robust identification approach compared to existing visual servoing design approaches are presented.