  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.
