A Face Quality Assessment System for Unattended Face Recognition: Design and Implementation

Abstract

This paper presents a face quality assessment approach that selects the highest-quality face image using a two-stage process from video streaming. In high-traffic environments, traditional face recognition methods can cause crowd congestion, emphasizing the need for unconscious face recognition, which requires no active cooperation from individuals. Due to the nature of unconscious face recognition, it is necessary to capture high-quality face images. In this paper, the FSA-Net head pose estimation network is enhanced to FSA-Shared_Nadam by replacing the Adam optimizer with Nadam and improving stage fusion. In the first stage, FSA-Shared_Nadam estimates head pose angles, MediaPipe detects facial landmarks to calculate eye distance and aspect ratios, and sharpness is calculated using the Laplacian operator. Images are considered valid if they meet the criteria. A model trains a face quality scoring formula, learning how different head pose angles affect face recognition accuracy. In the second stage, face images are clustered, and the formula is applied to select the highest-scoring face within each cluster. The approach was tested across multiple datasets, and a simulated security checkpoint scenario was created for practical testing. The results demonstrate the effectiveness of the FSA-Shared_Nadam head pose estimation algorithm and the proposed face quality assessment approach.