# Continuous Mobile User Authentication Using Combined Biometric Traits
## 3 Related Research
### Using Hidden Markov Model Based Classifiers  
One key advantage of systems relying on HMM is that a successful model can potentially be trained with only data from the user, to whom it belongs. Because of this, the model can be trained offline and the user's data does not have to be transmitted over the internet. Furthermore, the classifiers can be adapted continuously, since new input data can simply be retrained by fitting it to the existing model, without having to train the whole dataset again. In 2017, [PERSON] et al. [28] proposed a continuous user authentication system, utilizing a left to right HMM as classifier for training behavioral templates of different users [28]. The proposed method uses the Touchalytics database [26], and therefore also works with horizontal and vertical touch patterns. The HMM used for training uses a number of hidden states \(n\). Every state \(n_{x}\) is then modeled by a mixture of Gaussians and a left to right topology is used to model the underlying continuous touch input. At first, the dataset is normalized and then used for training the parameters of the HMM, which describe the user's touch input best, using the Baum-Welch expectation-maximization algorithm. Finding the optimum parameters is necessary since the number of hidden states and mixtures is dependent on the complexity and information of the training data. The number of states multiplied by mixtures should be significantly smaller than the average stroke length, so every Gaussian distribution has enough data. To make the training more robust, five-fold cross-validation is employed for finding the optimal model parameters. For every user, a behavioral model is created with the user's dataset. Short-term authentication can be made with a median Equal Error Rate (EER) of 0%, for inter-session authentication the median EER lies below 2% and for long-term authentication below 3%. The worst obtained EER values with 5%, 12%, and 28%, respectively, are also lower when using the HMM approach. [28]  
[PERSON] _et al._ proposed another solution in 2015, which extends the previously presented implementation, by using a multi-sensor approach combined with a newly generated database, for continuous user authentication [29]. Similar to [28], the study utilizes a HMM based training approach for authentication. Instead of only using touch data, the proposed solution employs touch, accelerometer, and gyroscope data for modeling tap and stroke patterns with a continuous left to right HMM. Analysis of the proposal shows that when combining multi-sensor data with touch information, authentication accuracy can be improved. The new dataset recorded for the study consists of multi-sensor data from 42 different users and includes unconstrained data, in contrast to [26; 28] where only limited horizontal and vertical gestures were used. The paper focuses on tap and slide touch gestures, since they are done most frequently, and ignores multi finger gestures like pinch or zoom. From those gestures, six raw features (timestamp, position, pressure, rotation, touch size, vibration) are extracted and the obtained sequences are normalized in regard to the sampling rate. Sequences recorded from touch inputs contain distinct spatio-temporal characteristics. Therefore, for every gesture (tap and slide) a distinct classifier has to be trained. However, in contrast to [28], where multiple classifiers are used for different slide directions, a single HMM is used for all directions by making the slide gestures rotation invariant. For every classifier, there are three HMM, where one is used for touch information and the other two for vibration and rotation data. The combined similarity score is calculated by averaging the likelihood scores of the models. The results achieved by the proposed solution are comparable to the system developed in [28], which requires eleven consecutive strokes (horizontal or vertical) to obtain a median EER of 0%.