## 1 Introduction  
Touch screen devices have evolved rapidly in recent years as demand and manufacture have skyrocketed. While smartphone capability continues to grow, progress in security has stagnated. This increasing gap between smartphone ability and security poses a significant problem. Today, smartphone users keep unprecedented amounts of sensitive data on their device, including photos, financial records, and private correspondence. Research into progressing the security of smartphone devices is necessary to protect the sensitive information of smartphone users. To counter this issue, researchers and manufacturers have invested time and resources into developing and improving different types of smartphone authentication methods. Currently, the most common method of authentication in today's mobile phones are knowledge-based methods such as a password or a personal identification number (PIN) [1]. Since this method relies on the user's own knowledge, it runs the risk of the user choosing an easy to remember password that can easy be stolen or lost [1]. Due to the insecurity of knowledge-based authentication, researchers have turned to other methods such as physiological and behavioral biometrics. Physiological biometrics involve the unique physical characteristics of an individual such as their face, fingerprints, or iris. Behavioral biometrics involve how a person interacts with their device such as their typing, swiping, or tapping patterns [2]. Biometrics authentication applies the user's unique biological or behavioral features to phone security, which is more difficult to replicate by attackers in comparison to knowledge-based authentication [1]. In recent years, biometrics-based authentication methods have shown promising results when tested with machine learning and deep learning algorithms. Deep learning algorithm-centric methods have gainedpopularity as of late due to recent tech advances enabling their efficiency, such as an increased availability of deep neural network (DNN) training datasets and increased computational power [3]. It has been found that biometrics-based authentication methods can more effectively secure mobile devices if more research is devoted to assuring that methodologies can be effective in real-world scenarios [4]. In this paper, the performance of machine learning and deep learning algorithms with biometrics-based authentication methods will be surveyed and analyzed. This subject has been investigated and reviewed by various researchers [4], thus this paper will continue this work by providing a comprehensive review on recent findings related to popular biometric methodologies with machine learning (ML) and deep learning (DL) algorithms. Our findings intend to help guide and inform researchers in their experiments relating to biometrics-based mobile security. This paper is organized as follows: The background section is introduced in Section 2. It will discuss the differences between physiological and behavioral biometric dynamics, presenting examples as well as their individual benefits and limitations. Various types of DL and ML algorithms that will be spotlighted in the paper will be discussed and explained on their uses and structures. The bulk of this paper includes the literature review (Section 3), which has been divided into two main sections: behavioral and physiological dynamics. Behavioral biometrics constitute Section 3.1, and physiological constitutes Section 3.2. The behavioral biometrics section is further split up into different behavioral characteristics utilized for authentication: Touch (Section 3.1.1. ), Motion (Section 3.1.2. ), Keystroke (Section 3.1.3. ), and Gait (Section 3.1.4). The physiological section follows the same format, with Face (Section 3.2.1. ), Ocular (Section 3.2.2. ), and other (Section 3.2.3.).