Unlock with Your Heart:Heartbeat-based Authentication on Commercial Mobile Phones.140:5 AuthenticatingHeartbeat Feature User Extract Scenario Infomation Segmentation Alignment Extraction Authentication Heartbeat Heart Rate Fine Template SVM Model Collection Estimation Generate Feature SVM Model Fine Alianmen Extractior Training Template Generation Fig.2.Authentication System Components process normally takes less than two minutes(for collecting 60 heartbeats).Users may be instructed to change the position or the angle of the device during the training process to introduce more variations in the training samples When collecting the training samples,our system records the built-in accelerometer readings at a sampling rate of 100~250 Hz(depending on the hardware support of the device).With the readings of the accelerometer,we first extract the heart rates and the body posture of the user.With this information,the collected training samples can be classified into one of the predefined scenarios,e.g,the heart rates are in the range of 50~80 Beats per Minute(BPM)and the user is sitting on a chair.The training samples are then used for generating heartbeat patterns for that given scenario.Each heartbeat pattern includes one heartbeat template for signal alignment and one Support Vector Machine(SVM)model for identifying the owner of the device.The SVM model is a two-class classifier that is trained using the training heartbeats from the owner(as the positive samples)and the benchmark heartbeats from a global heartbeat database(as the negative samples).The SVM model can give the likelihood whether an unknown heartbeat signal belongs to the owner or not. After the training process,our system uses the heartbeat patterns to perform user authentication.Similar to the training process,the authentication process first collects the heartbeat signals and then extracts the scenario information from the readings of the accelerometer.The scenario information is used for selecting one set of the heartbeat patterns,including both the template for signal alignment and the SVM model for authentication.If there is a matching heartbeat pattern in the database,the system first uses the template to segment the continuous SCG signals into individual heartbeat cycles and align the key features of each cycle.The system then extracts features using wavelet transform and applies the SVM model to classify the heartbeats.If there is no heartbeat pattern for the identified scenario,the system fallbacks to another authentication scheme,such as asking the user to input a PIN.If the user is authenticated through the PIN,the buffered heartbeat signals are used for generating the new heartbeat pattern(both the alignment template and the SVM model)for the identified scenario. The key components of our system are described in the following sections: Heartbeat Segmentation and Alignment(Section 4):In the heartbeat segmentation component,we use a two-step segmentation algorithm to divide the continuous acceleration signals into individual heartbeat cycles. The first step is coarse heart rate estimation,which uses a coarse template to estimate the heart rates from the accelerometer readings.The estimated heart rates are used for selecting the heartbeat pattern which contains the template for fine-grained heartbeat alignment.In the second step of heartbeat segmentation,we use the fine template to perform a cross-correlation on the continuous heartbeat signals.By this way,we can precisely align the key features of each heartbeat cycle in the time domain. Feature Extraction(Section 5):After the segmentation step,our system performs data preprocessing,e.g., normalizing the amplitude of the heartbeat signals,before the feature extraction step.Then,we use Discrete Wavelet Transform(DWT)to extract features from the heartbeat.Each heartbeat cycle is decomposed into multiple levels of wavelet coefficients,and we choose the wavelet coefficients that are most closely related to the heartbeat patterns.This way,we reduce noises that come from different sources,including the respiration movements,small limb movements,and small variations in accelerometer readings. Proc.ACM Interact.Mob.Wearable Ubiquitous Technol..Vol.2.No.3.Article 140.Publication date:September 2018.Unlock with Your Heart: Heartbeat-based Authentication on Commercial Mobile Phones • 140:5 Heartbeat Collection Training Extract Scenario Infomation Heart Rate Estimation Posture Estimation Scenario Selection Authenticating Heartbeat Segmentation & Alignment Generate Fine Alignment Template Feature Extraction User Authentication Feature Extraction SVM Model Generation Fine Template SVM Model Fig. 2. Authentication System Components process normally takes less than two minutes (for collecting 60 heartbeats). Users may be instructed to change the position or the angle of the device during the training process to introduce more variations in the training samples. When collecting the training samples, our system records the built-in accelerometer readings at a sampling rate of 100∼250 Hz (depending on the hardware support of the device). With the readings of the accelerometer, we first extract the heart rates and the body posture of the user. With this information, the collected training samples can be classified into one of the predefined scenarios, e.g., the heart rates are in the range of 50 ∼ 80 Beats per Minute (BPM) and the user is sitting on a chair. The training samples are then used for generating heartbeat patterns for that given scenario. Each heartbeat pattern includes one heartbeat template for signal alignment and one Support Vector Machine (SVM) model for identifying the owner of the device. The SVM model is a two-class classifier that is trained using the training heartbeats from the owner (as the positive samples) and the benchmark heartbeats from a global heartbeat database (as the negative samples). The SVM model can give the likelihood whether an unknown heartbeat signal belongs to the owner or not. After the training process, our system uses the heartbeat patterns to perform user authentication. Similar to the training process, the authentication process first collects the heartbeat signals and then extracts the scenario information from the readings of the accelerometer. The scenario information is used for selecting one set of the heartbeat patterns, including both the template for signal alignment and the SVM model for authentication. If there is a matching heartbeat pattern in the database, the system first uses the template to segment the continuous SCG signals into individual heartbeat cycles and align the key features of each cycle. The system then extracts features using wavelet transform and applies the SVM model to classify the heartbeats. If there is no heartbeat pattern for the identified scenario, the system fallbacks to another authentication scheme, such as asking the user to input a PIN. If the user is authenticated through the PIN, the buffered heartbeat signals are used for generating the new heartbeat pattern (both the alignment template and the SVM model) for the identified scenario. The key components of our system are described in the following sections: Heartbeat Segmentation and Alignment (Section 4): In the heartbeat segmentation component, we use a two-step segmentation algorithm to divide the continuous acceleration signals into individual heartbeat cycles. The first step is coarse heart rate estimation, which uses a coarse template to estimate the heart rates from the accelerometer readings. The estimated heart rates are used for selecting the heartbeat pattern which contains the template for fine-grained heartbeat alignment. In the second step of heartbeat segmentation, we use the fine template to perform a cross-correlation on the continuous heartbeat signals. By this way, we can precisely align the key features of each heartbeat cycle in the time domain. Feature Extraction (Section 5): After the segmentation step, our system performs data preprocessing, e.g., normalizing the amplitude of the heartbeat signals, before the feature extraction step. Then, we use Discrete Wavelet Transform (DWT) to extract features from the heartbeat. Each heartbeat cycle is decomposed into multiple levels of wavelet coefficients, and we choose the wavelet coefficients that are most closely related to the heartbeat patterns. This way, we reduce noises that come from different sources, including the respiration movements, small limb movements, and small variations in accelerometer readings. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 2, No. 3, Article 140. Publication date: September 2018