Methodologies of Audio-Visual Biometric Performance Evaluation for the H2020 SpeechXRays Project

Biometric recognition is nowadays widely used in different services and applications, making the user authentication easier and more secure than the traditional authentication system. Starting from this idea, the EU SpeechXRays project H2020 developed and evaluated in real-life environments a user recognition platform based on face and voice modalities. Since the proposed biometric solution was evaluated in real-life environments where biometric data recorded was not accessible because of the General Data Protection Regulation GDPR, the ground truth of the conducted evaluation was not available. To correctly report the performance evaluation, some methodologies were proposed to detect the errors caused by the absence of ground truth. This paper describes the biometric solution provided by the project and presents the biometric performance evaluation carried out in three real-life use case pilots on more than 2 000 users.

I. Introduction

A biometric system is based on a pattern-recognition system that recognizes a person based on a feature vector extracted from a specific physiological or behavioral characteristic [1]. The choice of the biometric characteristic depends on the application. Most biometric systems deployed in real-world applications are unimodal, relying on evidence of a single source of information for authentication. However, unimodal biometric systems often face significant limitations due to noise sensitivity, intra-class variability, data quality, nonuniversality, privacy issues [2] and other factors. Some of the limitations imposed by unimodal biometric systems can be overcome by including multiple sources of information to establish identity. Such systems, known as multimodal biometric systems [3], can be more reliable and robust to the attacks of impostors. These systems help achieve an improvement in biometric performance that may not be possible using a unimodal biometric system.