Behavioral Hand Waving Biometrics

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Behavioral Hand Waving Biometrics

Mohana Priya A

Assistant Professor,

M.E (Computer Science Engineering), M.Kumarasamy college of Engineering,

Karur, Tamilnadu, India,

Alamelu V

Asst Professor,

M.E (Computer Science Engineering), M.Kumarasamy college of Engineering,

Karur, Tamilnadu, India,

Abstract:- Mobile devices, such as smart phones and tablets, offer a wide variety of important services to everyday users. Many of these services are highly sensitive and can be abused by malicious entities, without the knowledge of the device user, in the form of insider attacks (such as malware) and/or outsider attacks (such as unauthorized reading and relay attacks). In this paper, present a novel application authentication approach that can be used to protect mobile device from unauthorized access. It captures users intent to access the service via a lightweight hand waving gesture. This gesture is very simple, quick and intuitive for the user, but would be very hard for the attacker to exhibit without users knowledge. Then present the design and implementation of a hand waving gesture recognition mechanism using accelerometer and classify the patterns using neural network classifiers. We also report on experiments to analyze the performance of our approach both in benign and adversarial settings. This result indicates the approach to be quite effective in preventing the misuse of unlocking phones while imposing only minimal user burden.

Keywords: Adversary Settings, Behavioral biometrics, Classification, Gestures, Unlocking patterns.


Smart mobile devices have become essential tools in many peoples daily lives. Not only are we using these devices as the means to communicate with others, as sources of entertainment and as ways of expressing ourselves, but we also use them to store sensitive personal information and access different online services. Despite all the information contained in a device and the transactions that can be performed with it, many users choose not to protect their devices and at the same time they tend to be perpetually logged into some of the services provided by mobile third party applications. Thus, an attack on the mobile device, or the loss of it, can have negative consequences such as the intrusion of privacy, the opportunity to impersonate users, and even severe financial loss. Currently, most of the solutions for authenticating users into their devices and other mobile services are based on the same solutions offered when using desktop computers, which usually involve the use of a PIN, a strong password, or some sort of extra external security token device. These techniques become cumbersome when applied to mobile devices and do not always provide a satisfactory user experience. Besides, they are not a sustainable approach for the future of mobile interactions, in which people would carry only one secure trustable device to perform most operations and would preferably use only one hand to operate such device.

A biometric-based re-authentication system involves an enrollment phase and a re-authentication phase. Behavioral biometrics assumes that people have distinct stable patterns on a certain behavior, such as waving patterns. Behavioral biometric-based re-authentication uses the behavior patterns to authenticate a users identity. A user is enrolled by providing his hand waving patterns. The system learns patterns from the provided data and stores the learned patterns for future reference. During the re- authentication phase, the system compares the observed waving data against the stored data to re-authenticate a user.


    N. Sae-Bae et.al, [1] proposed a method based on multi- touch gestures and can be seen as an instance of a behavioral-biometric-based authentication technique. It is not susceptible to shoulder surfing or finger oil attacks and potentially provides significantly large entropy. The user performs the gesture with all five fingers at once, and biometrics is drawn from the hands geometry as well as the dynamics of the gesture itself. Thus far, users have readily accepted multi-touch gestures in the interface, and much has been made of the accessibility of this mode of interaction to a broad user public.

    E. Miluzzo et.al, [2] found that sensor information from mobile devices specifically from the accelerometer and gyroscope can be adequate to infer the locations of touch-screen taps. Interpreted differently, a background process running on a mobile device may be able to silently monitor the accelerometer and gyroscope signals, and infer what the user is typing on the software keyboard.

    Davrondzhon Gafurov, et.al,[3] implemented a system that contains identification mode, the system recognizes an individual by searching the enrolment samples of all users in the database for a match. The genuine attempt is a self verification attempt when a users submitted sample is compared to his own enrolment sample in the database. The impostor attempt is a non self- verification attempt when users verification sample is compared against another users enrolment sample.

    Jiayang Liu, et.al, [4] proposed a gesture-based authentication using uWave, a state-of-the art gesture recognition system based on a single tri-axis accelerometer and distinguish two different objectives of user authentication. For privacy-insensitive data, the objective of user authentication is to retrieve user-specific data

    instead of protecting them, e.g. personal profiles or personalized configurations on a TV remote shared by family members.

    Fuminori Okumura, et.al, [5] implemented a system, just need to grasp and shake cellular phones. This advantage is especially important for cellular phones. This authentication is strong against injustice and right malicious imitation. It is well known that we can counterfeit fingerprint authentication or using someone's hair in DNA certification.


    In stock Android, every user has six different options to choose from lock screen, all of which offer their levels of security. If a user has a non-stock Android device like the Galaxy S3, then there are some differences in functionality but for the most part they all act in a similar fashion. First, to access the lock screen options, the universal location tends to be in Settings-Security. From there, one should see an option towards the top called Screen lock, which then takes us to the lock screen options once tapped.


        Slide is probably the most commonly used lock screen of all its basically the default. This lock screen is not secure by any means, and only asks that the user of the phone grab the circle with a lock inside and slides it outside of a larger circle to unlock the phone. There are no passwords or patterns; its simply a way to keep the phone from turning itself on and then accessing all sorts of info in the pocket or purse without your knowing. The nice thing about using Slide is that one can still access the notifications pull down without having to fully unlock the phone. None of the other lock screen options allow for this, as they are technically secure

      2. FACE UNLOCK:

        Face Unlock was introduced back in Ice Cream Sandwich as a fun way to unlock the phone using a face of the user. In order to set this option up, one has to place his face inside of a face shaped ring of dots using front facing camera until the device decides that the face is enough to be able to unlock with it. Once approved, a user will be asked to provide a backp option in case the device cannot recognize his face. The two backup options are PIN or pattern.


        Pattern, PIN and Password unlocks are exactly as they sound. One should either create a pattern, a numeric PIN, or an alpha-numeric password that needs to be entered in order to unlock the phone. These are likely the most secure of them all. If a user forgets the pattern, PIN, or password, then he is not allowed to access the phone.


    Fingerprint scanning technology is becoming increasingly important with everyday security measures and can provide an affordable, effective and reliable means of identification. Atrix smart phone, made by Motorola supplies a finger scanning system. Motorola Atrix 4G has a feature called Fingerprint Scanner. Overlapping processes

    on the screen and low speed are the main problems in this system.


    The main hypothesis of this study is that continuously recorded touch data from a touch screen is distinctive enough to serve as a behavioral biometric. The smart phone records times, finger pressures, and the screen areas covered by each finger. A continuous authentication application could run in the background and extract multiple features from all available raw input. This raw input is readily available through the phones API. Based on various extracted features, the system can then learn a profile of the legitimate user and compare all screen interaction with this profile.


        The Lock Screen consists of six circles. Each circle changes its color maximum of seven times by retouching the circle. There is no specific order for touching the circles. Once retouching is done a password string is generated. This password string is then confirmed by clicking on ok button. If the string is matched then the phone is unlocked. The existing locking patterns are illustrated in fig 1.

        Fig 1. Existing locking patterns


    A handshaking biometric-based approach, called OpenSesame is used to unlock the smart phone. For precisely characterizing users shaking actions, selecting appropriate sensors is necessary. In this technique the 3- axis accelerometer is used for detecting the hand shaking motion. The accelerometer allows smart phones to detect the motion performed on them. The accelerometer in smart phones measures the acceleration of the phone relative to freefall. The accelerometer measures the acceleration of the phone in three different axes: X, Y, and Z. Based on these features, we propose a system with Open Sesame

    framework with neural network pattern classifiers. OpenSesame which consists of four components: sensing, filter, fetcher, classifier, and matcher.

    Sensing: This component is straightforward used to record the users hand waving action data.

    Filter: In practice, we find that there always exist some silent periods when no waving or very low level sensing data is detected. For better feature extraction, we use filter component to wipe out the silent periods.

    Fetcher: The filtered raw tuples is fed into fetcher component in which four waving functions are applied to fetch the waving features.

    Classifier: To discriminate the authorized users and unauthorized users, neural network classifier is employed in our system for classification.

    Matcher: In the last component, the extracted feature is used to determine whether it matches the predefined one. The proposed framework is defined in fig 2.

    Fig 2: Unlocking using Hand waving biometrics


We can analyze the proposed system using performance metrics such as sensitivity, specificity that contains true positive rate, false positive rate, true negative rate and false negative rate and these parameters are defined as follows:

  • Sensitivity (also called the true positive rate or the recall in some fields) measures the proportion of positives that are correctly identified as such (e.g., the percentage of sick people who are correctly identified as having the condition).

  • Specificity (also called the true negative rate) measures the proportion of negatives that are correctly identified as such (e.g., the percentage of healthy people who are correctly identified as not having the condition). Thus sensitivity quantifies the avoiding of false negatives, as specificity does for false positives.

    • True positive rate (or sensitivity): TPR=TP/(TP+FN)

    • False positive rate: FPR=FP/ (FP+TN)

    • True negative rate (or specificity): TNR=TN/ (FP+TN)

Based on the above measurements, our proposed system provides increase TPR and reduces FPR and TNR. The proposed performance is analyzed in fig 3.












NN classifiers

SVM classifiers KNN classifiers


Fig 3. Performance evaluation


To prevent unauthorized usage of smart phones, a re- authentication system is more suitable than an authentication system. An authentication system authenticates a user for one time when he logs in, such as inputting a password to unlock a smart phone. The purpose of a re-authentication system is to continuously authenticate the current user during the whole system execution. In order to the system performs continuous re- authentication and does not need human assistance during re-authentication. We have discussed hand waving biometric feature design and selection for waving patterns. We have demonstrated the effectiveness and efficiency of our system in extensive experiments. In this project we propose a novel behavioral biometric-based authentication approach called OpenSesame for smart phone. We design four waving functions to fetch the unique pattern of users hand waving actions. By applying the neural network classifier, the smart phone can accurately verify the authorized user with the pattern of hand waving action.


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