In this work, we consider the applicability of the face recognition algorithms to the data obtained from a dynamic vision sensor. A basic method using a neural network model comprised of reconstruction, detection, and recognition is proposed that solves this problem. Various modifications of this algorithm and their influence on the quality of the model are considered. A small test dataset recorded on a DVS sensor is collected. The relevance of using simulated data and different approaches for its creation for training a model was investigated. The portability of the algorithm trained on synthetic data to the data obtained from the sensor with the help of fine-tuning was considered. All mentioned variations are compared to one another and also compared with conventional face recognition from RGB images on different datasets. The results showed that it is possible to use DVS data to perform face recognition with quality similar to that of RGB data.
Adaptive Temporal Pooling for Object Detection using Dynamic Vision Sensor
