AI-Assisted Verification of Biometric Data Collection
This work addresses the problem of deploying action recognition on diverse mobile hardware for applications like biometric data collection, but it is incremental as it primarily compares existing methods.
The paper tackled the challenge of recognizing actions from video on older Android phones by comparing YOLO architecture performance across devices with and without GPUs, achieving results on a custom dataset but noting limitations in face and action recognition on limited hardware.
Recognizing actions from a video feed is a challenging task to automate, especially so on older hardware. There are two aims for this project: one is to recognize an action from the front-facing camera on an Android phone, the other is to support as many phones and Android versions as possible. This limits us to using models that are small enough to run on mobile phones with and without GPUs, and only using the camera feed to recognize the action. In this paper we compare performance of the YOLO architecture across devices (with and without dedicated GPUs) using models trained on a custom dataset. We also discuss limitations in recognizing faces and actions from video on limited hardware.