An Efficient Method for Face Quality Assessment on the Edge
This work addresses the need for efficient face quality assessment in edge-based face recognition systems, though it is incremental as it builds on existing landmark detection networks.
The paper tackles the problem of prioritizing face detections for recognition on edge devices by proposing a face quality score regression method that adds a single layer to a landmark detection network, achieving efficiency with almost no additional cost and showing competitive performance in experiments.
Face recognition applications in practice are composed of two main steps: face detection and feature extraction. In a sole vision-based solution, the first step generates multiple detection for a single identity by ingesting a camera stream. A practical approach on edge devices should prioritize these detection of identities according to their conformity to recognition. In this perspective, we propose a face quality score regression by just appending a single layer to a face landmark detection network. With almost no additional cost, face quality scores are obtained by training this single layer to regress recognition scores with surveillance like augmentations. We implemented the proposed approach on edge GPUs with all face detection pipeline steps, including detection, tracking, and alignment. Comprehensive experiments show the proposed approach's efficiency through comparison with SOTA face quality regression models on different data sets and real-life scenarios.