3D Password

Abstract

The 3D passwords which are more customizable and very interesting way of authentication. Now the passwords are based on the fact of Human memory. Generally simple passwords are set so as to quickly recall them. The human memory, in our scheme has to undergo the facts of Recognition, Recalling, Biometrics or Token based authentication. Once implemented and you log in to a secure site, the 3D password GUI opens up. This is an additional textual password which the user can simply put. Once he goes through the first authentication, a 3D virtual room will open on the screen. In our case, let's say a virtual garage

The 3D password is a multi factor authentication scheme. The 3D password presents a 3D virtual environment containing various virtual objects. The user navigates through this environment and interacts with the objects. The 3D password is simply the combination and the sequence of user interactions that occur in the 3D virtual environment. The 3D password can combine recognition, recall, token, and biometrics based systems into one authentication scheme. This can be done by designing a 3D virtual environment that contains objects that request information to be recalled, information to be recognized, tokens to be presented, and biometric data to be verified.

Refer

3d-Password Report 1

3d-Password Report 2

3d-passwords ppt 1

3d-password ppt 2

3D PASSWORD

3D Secure Password

hai

Face Recognition Technology

Abstract

Recently face recognition is attracting much attention in the society of network multimedia information access. Areas such as network security, content
indexing and retrieval, and video compression benefits from face recognition technology because "people" are the center of attention in a lot of
video. Network access control via face recognition not only makes hackers virtually impossible to steal one's "password", but also increases the
user-friendliness in human-computer interaction. Indexing and/or retrieving video data based on the appearances of particular persons will be useful
for users such as news reporters, political scientists, and moviegoers. For the applications of videophone and teleconferencing, the assistance of
face recognition also provides a more efficient coding scheme.

 Refer:
Face-recognition-technology-ppt-1
Face-Recognition-Technology-Seminar-Report1

Face Recognition Technology seminar report 

Facial recognition system

Software of Face Recognition Technology
Facial recognition software falls into a larger group of technologies known as biometrics. Biometrics uses biological information to verify identity. The basic idea behind biometrics is that our bodies contain unique properties that can be used to distinguish us from others. Besides facial recognition, biometric authentication methods also include:
" Fingerprint scan
" Retina scan
" Voice identification
Facial recognition methods generally involve a series of steps that serve to capture, analyze and compare a face to a database of stored images. The basic processes used by the FaceIt system to capture and compare images are:
1.Detection - When the system is attached to a video surveillance system, the recognition software searches the field of view of a video camera for faces. If there is a face in the view, it is detected within a fraction of a second. A multi-scale algorithm is used to search for faces in low resolution. The system switches to a high-resolution search only after a head-like shape is detected.
2. Alignment - Once a face is detected, the system determines the head's position, size and pose. A face needs to be turned at least 35 degrees toward the camera for the system to register it.
3. Normalization -The image of the head is scaled and rotated so that it can be registered and mapped into an appropriate size and pose. Normalization is performed regardless of the head's location and distance from the camera. Light does not impact the normalization process.
4. Representation - The system translates the facial data into a unique code. This coding process allows for easier comparison of the newly acquired facial data to stored facial data.
5. Matching - The newly acquired facial data is compared to the stored data and (ideally) linked to at least one stored facial representation.