Deep learning based authentication schemes for smart devices in different modalities: progress, challenges, performance, datasets and future directions

• Published in: Multimedia tools and applications 2024-02, Vol.83 (28), p.71451-71493
• Main Authors: Shende, Shailendra W., Tembhurne, Jitendra V., Ansari, Nishat Afshan
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Authentication; Biometric identification; Biometrics; Business metrics; Cognitive tasks; Computer Communication Networks; Computer Science; Computer vision; Data Structures and Information Theory; Datasets; Deep learning; Electronic devices; Image processing; Impact analysis; Machine learning; Multimedia Information Systems; Natural language processing; Neural networks; Performance measurement; Physiology; Recurrent neural networks; Smart devices; Special Purpose and Application-Based Systems; Taxonomy; Unsupervised learning
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-024-18350-5

Deep learning algorithms have shown exceptional effectiveness in a wide range of supervised and unsupervised learning tasks in a variety of fields, including image processing, computer vision, natural language processing, and speech or voice processing. In this paper, a comprehensive analysis is conducted to assess the impact of deep learning on user authentication using both physiological and behavioural biometrics. This work encompasses the diverse deep learning approaches employed in authentication schemes tailored for smart devices. Meticulous scrutiny of commonly used datasets in these authentication studies is undertaken, accompanied by a comparative assessment of performance. The deep learning models under consideration span a spectrum of architectures, including deep neural networks, convolutional neural networks, deep auto-encoders, recurrent neural networks, and their variants. To enhance the clarity and categorization of authentication techniques for smart devices, a taxonomy is proposed based on the specific authentication metrics employed: (1) Knowledge-based Authentication (KBA), (2) Physiological Biometric-based Authentication (PBBA), (3) Behavioural Biometric-based Authentication (BBBA), (4) Physiological and Behavioural Continuous Authentication (PBBCA), and (5) Multi Modal Authentication (MMA). Furthermore, potential contributions of deep learning techniques to the realms of physiological and behavioural biometrics are discussed. Significance is placed on performance metrics, including accuracy, stability, and robustness, in evaluating these deep learning-based authentication systems. The challenges and limitations that deep learning approaches must surmount when dealing with real-world biometric data in the context of biometric identification systems are examined. This work not only underscores the transformative role of deep learning in user authentication but also offers valuable insights into the evolving landscape of biometric identification on smart devices. An examination of performance metrics provides a holistic view of the strengths and areas for improvement in deep learning based authentication solutions.