Multi touch Gesture-Based Authentication
ABSTRACT:Safety is a major focus of awareness for operators and users of the website
and its many applications, among the difficult problems still inefficiently addressed
is identity authentication for purposes of associating a particular user with
particular services and authorizations. A request is a way to classify users such that
forging recommendation is difficult for adversaries, while providing strong
authentication of their chosen identifiers remains easy and convenient for
users.Password authentication is a common approach to system security. The
conventional verification table approach has significant drawbacks. Recently,
neural networks have been used for password authentication to overcome the
shortcomings of traditional approaches. In neural network approaches to password
authentication, no verification table is needed; rather, encrypted neural network,
weights are stored within the system.Existing layered neural network techniques
have their limitations such as long training time and recall approximation. In
comparison to existing layered neural network techniques, the proposed method
provides better accuracy and quicker response time to registration and password
changes. Two different studies were performed to evaluate the concept. First, a
single session experiment was performed in order to explore feasibility of
multitouch gestures for user authentication. Testing on the canonical set showed
that the system could achieve Good performance. The system derives rotation and
translation invariant features to represent the gesture. Multi-touch gesture samples
captured from users as described above were used to study the discriminative
power of each of the 22 gestures.
Existing System: Computer security has been one of the most important issues in the
information technology era.
 Among many computer access control techniques, password authentication
has been widely used for a long time, and is still one of the most convenient
authentication mechanisms today.
 A common password authentication approach is the use of verification
tables. Using this approach, the password PWk provided by user k is
encoded through a one-way hash function or encryption algorithm,
 However, in an open access environment, an intruder is still able to make
modification to the verification table.
Disadvantages: The main problem is related to users selecting weak textual passwords.
 A dissimilarity score is computed from all pair wise distances between an
input test gesture.
 The system wrongly rejects a gesture that comes from a genuine and honest
user is called False Rejection Rate.
Proposed System: What we seek is a way to identify users such that forging credentials is
difficult for adversaries, while providing strong authentication of their
chosen identifiers remains easy and convenient for users.
 A layered neural network scheme has been proposed for password
authentication. According to this, a desired binary integer vector (e.g., [0,
0, 1, 1]) is assigned to each user.
 Main concept of o Pass is free users from having to remember or type
any passwords into conventional computers for authentication.
 In comparison to existing layered neural network techniques, the
proposed method provides better accuracy and quicker response time to
registration and password changes.
Advantages: The proposed method provides better accuracy and quicker response
time.
 A set of desired integer vectors along with the corresponding user IDs
with encrypted passwords are used to train the layered neural network.
 A gesture is defined as either a static palm gesture or dynamic palm
gesture depending on whether or not a user’s palm is moving while
executing the gesture.
Hardware Requirements:
SYSTEM
: Pentium IV 2.4 GHz

HARD DISK
: 40 GB

RAM
: 256 MB
Software Requirements:
Operating system
: Windows 7

IDE
: Microsoft Visual Studio 2010

Coding Language
: C#.NET.