An Open-Source Face-Aware Capture System

Abstract

Poor-quality facial images pose challenges in biometric authentication, especially in passport photo acquisition and recognition. This study proposes a novel and open-source solution to address these issues by introducing a real-time facial image quality analysis utilizing computer vision technology on a low-power single-board computer. We present an open-source complete hardware solution that consists of a Jetson processor, a 16 MP autofocus RGB camera, a custom enclosure, and a touch sensor LCD for user interaction. To ensure the integrity and confidentiality of captured facial data, Advanced Encryption Standard (AES) is used for secure image storage. Using the pilot data collection, the system demonstrated its ability to capture high-quality images, achieving 98.98% accuracy in storing images of acceptable quality. This open-source, readily deployable, secure system offers promising potential for diverse real-time applications such as passport verification, security systems, etc.

AI Face-Capture for First-time Passport Application

“…The system detects the face using dlib and detects facial landmark points using a 68 landmark points detector [17,18]. After detecting the landmark points, geometric and photographic checks are performed, as denoted in [3]. Under the category of geometric tests, parameters such as eye distance, vertical and horizontal position, head image width, and height ratios are measured to meet specified criteria.…”

“…In a previous project focused on adult passport photo capture, we implemented 68 landmark points and checked face image quality [3]. During this development, we found that automatic camera adjustment with face position would significantly aid in collecting data from children.…”

“…Camera-based solutions are widely used for various modalities in biometric systems [7][8][9]. For image analysis-based projects, single-board computers such as Jetson Nano [3,[9][10][11], Google Coral [12,13], and Raspberry Pi [14] are popular for their smaller size and low power consumption. Our study used a Jetson Nano processor with a commercially available camera.…”

AI-Powered Camera and Sensors for the Rehabilitation Hand Exoskeleton

“…Given the widespread use of camera-based solutions across different systems [12,13], we have adopted this approach in our design. For projects focused on image analysis, single-board computers like the Jetson Nano [14][15][16][17][18], Google Coral [19,20], and Raspberry Pi [21][22][23] are favored for their compact size and low power consumption. To optimize our system, we have used the Google Coral Dev Board Mini to run image processing and control peripherals because of its smaller size and inclusion of a Tensor Processing Unit (TPU) for faster processing of deep learning models.…”