## 1 Introduction  
The Internet's spread as a computer network, driven by its characteristics including its ability to include numerous devices and geographic areas, has changed the way information is spread. With the increase in functionality of mobile devices, we are transitioning from an Internet society towards a mobile one. Google's report \"How People Use Their Devices\" [1] shows that smartphone usage during the day dominates over computer usage, as seen in the graph in Figure 1. Based on the devices on which searches are made during the day, Google also found out that although the usage of computers becomes dominant from around 8 a.m. when people might start their workday, mobile usage takes the lead again in the late afternoon, and continues to increase in the evening, as presented in Figure 2.  
However, besides offering a huge amount of information and other numerous functionalities, mobile devices also contain sensitive user data for personalization of user experience in applications or to offer more efficient services. Controlling access in computer devices through user authentication is one of the means of securing the data that these devices contain. Figure 1: Daily device usage [1]. Figure 2: Search volume by device during an average day [1]. Secure authentication for smart phones is becoming important for many applications such as financial transactions. So far, Personal Identification Number (PIN) and password authentication are the most commonly used methods for smartphone access control. Specifically, for a PIN and limited length password, the level of security is low and thus can be compromised easily. The techniques of using the smartphone's front camera for face recognition is suggested. [PERSON] et al. suggest an optimized Sparse Representation Classification for face authentication [2]. Sparse Representation Classification (SRC) is a state-of-the-art face-recognition algorithm, which has been shown to outperform many classical face-recognition algorithms. The success of SRC is due to its use of 1 optimization, which makes SRC robust to noise and occlusions [2]. Because the former technique requires another step of authentication, [PERSON] et al. proposes a two-factor face authentication scheme using matrix transformations and a user password [3]. This scheme is designed with a secure cancellation feature, in that templates composed of permutation and feature vectors can be freely changed [3]. Numerous other authentication methods for smartphones have been explored [4]. Advancements in data analysis and machine learning have made it possible to explore other methods of achieving user authentication, such as authentication based on biometric signs. Biometric signs represent measurable biological characteristics of a human being. On the other hand, a biometric process represents the automated method of recognizing an individual based on the identification of biometric signs [5]. The human face plays an important role in daily social interaction. Face recognition is a natural process for humans. Even newborn babies have the ability to differentiate between known faces [6]. How hard could it be for computer devices? This question sets the foundation of the field of automatic face recognition, one of the most researched topics in computer vision and pattern recognition. In addition to applications related to identification and verification, such as access control and surveillance, face recognition has been useful in human-computer interaction, virtual reality and computer entertainment [7]. A study from Juniper Research [8] found that there would be a rapid growth in biometric authentication usage, with voice and facial recognition leading the way. The study predicts that biometric authentication usage would grow from 190 million mobile devices in 2016 to 600 million by 2021. Because of the simpler hardware implementation for voice and face recognition, it is predicted to overcome the usage of fingerprints for authentication. Furthermore, authentication by the use of multiple biometrics is predicted.