Biometric recognition is a trending technology that uses unique characteristics data to identify or verify/authenticate security applications. Amidst the classically used biometrics, voice and face attributes are the most propitious for prevalent applications in day-to-day life because they are easy to obtain through restrained and user-friendly procedures. The pervasiveness of low-cost audio and face capture sensors in smartphones, laptops, and tablets has made the advantage of voice and face biometrics more exceptional when compared to other biometrics. For many years, acoustic information alone has been a great success in automatic speaker verification applications. Meantime, the last decade or two has also witnessed a remarkable ascent in face recognition technologies. Nonetheless, in adverse unconstrained environments, neither of these techniques achieves optimal performance. Since audio-visual information carries correlated and complementary information, integrating them into one recognition system can increase the system's performance. The vulnerability of biometrics towards presentation attacks and audio-visual data usage for the detection of such attacks is also a hot topic of research. This paper made a comprehensive survey on existing state-of-the-art audio-visual recognition techniques, publicly available databases for benchmarking, and Presentation Attack Detection (PAD) algorithms. Further, a detailed discussion on challenges and open problems is presented in this field of biometrics.
翻译:生物测定识别是一种趋势式技术,它使用独特的特征数据来识别或核查/验证/验证安全应用。在古典使用的生物鉴别学应用中,声音和面貌特征最有利于日常生活中普遍应用,因为通过限制和方便用户的程序很容易获得。由于在智能手机、笔记本电脑和平板电脑中广泛使用低成本的音频和面部捕捉传感器,使声音和面部捕捉传感器与其他生物测定技术相比更加具有优势。多年来,光是声学信息在自动演讲者核查应用方面就取得了巨大成功。在前10年或2年的时间里,面对识别技术也出现了显著的飞跃。然而,在不利的不受限制的环境中,这些技术都没有取得最佳的性能。由于视听信息具有关联性和互补性,将其纳入一个识别系统可以提高系统性能。生物测定技术在演示攻击和视听数据用于检测这类攻击方面的脆弱性也是一项热门的研究课题。本文对目前状态的视听识别技术进行了全面调查。在地面上还存在一个公开的详细数据库,用于基准测定和演示的实地分析。