UTTAR PRADESH TECHNICAL UNIVERSITY

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UTTAR PRADESH TECHNICAL UNIVERSITY
IIMT COLLEGE OF ENGINEERING GREATER NOIDA

Title: Face Recognition Technology

Team: Team members: Gaurav Tiwari Gyanendra Shekhar B.Tech (CS-VI-A) Address for correspondence: E-197 , Gamma-1st, Gr. Noida Phn: 9450696163, 9015217941 e-mail:[email protected] [email protected]

Ojectives/aim:
This technology aims at developing a highly cost effective tool to recognize faces of people like criminals. Criminals can easily change their identity, so this technology will help in security & recognition of criminals as they are not much capable to change their faces. It can easily be customized as per requirement and availability of information about most known criminals. Their faces can be the target face for recognition.

Technical details:
Face recognition is a natural human ability and a widely accepted identification and authentication method. In modern legal settings, a lot of credence is placed on identifications made by eyewitnesses. Consequently these are based on human perception which is often flawed and can lead to situations where identity is disputed. Therefore, there is a clear need to secure identifications in an objective way based on anthropometric measures. Anthropometry has existed for many years and has evolved with each advent of new technology and computing power. As a result of this, face recognition methodology has shifted from a purely 2D image-based approach to the use of 3D facial shape. However, one of the main challenges still remaining is the non-rigid structure of the face, which can change permanently over varying time-scales and briefly with facial expressions. The majority of face recognition methods have been developed by scientists with a very technical background such as biometry, pattern recognition and computer vision. This article strives to bridge the gap between these communities and the forensic science end-users. A concise review of face recognition using 3D shape is given. Methods using 3D shape applied to data embodying facial expressions are tabulated for reference. From this list a categorization of different strategies to deal with expressions is presented. The underlying concepts and practical issues relating to the application of each strategy are given, without going into technical details. The discussion clearly articulates the justification to establish

archival, reference databases to compare and evaluate different strategies.

Innovatives and usefulness:
Simultaneous multiple face processing. Our biometric face recognition system performs fast and accurate detection of multiple faces in live video streams and still images. All faces on the current frame are detected in 0.07 sec. and then each face is processed in 0.13 sec. Live face detection. A conventional face identification system can be easily cheated by placing a photo of another person in front of a camera. Our face recognition system is able to prevent this kind of security breach by determining whether a face in a video stream belongs to a real human or is a photo Face image quality determination. A quality threshold can be used during face enrolment to ensure that only the best quality face template will be stored into database. Tolerance to face posture. face recognition system has certain tolerance to face posture that assures face enrolment convenience: rotation of a head can be up to 10 degrees from frontal in each direction (nodded up/down, rotated left/right, tilted left/right). Multiple samples of the same face. Biometric template record can contain multiple face samples belonging to the same person. These samples can be enrolled with different face postures and expressions, from different sources and in different time thus allowing to improve matching quality. Identification capability. system functions can be used in 1-to-1 matching (verification), as well as 1-to-many mode (identification). Fast face matching. face template matching algorithm compares 100,000 faces per second.

Market Potential And Competitive Advantages:

In the past, the primary users of facial recognition software have been law enforcement agencies, who used the system to capture random faces in crowds. Some government agencies have also been using the systems for security and to eliminate voter fraud. The U.S. government has recently begun a program called US-VISIT (United States Visitor and Immigrant Status Indicator Technology), aimed at foreign travelers gaining entry to the United States. When a foreign traveler receives his visa, he will submit fingerprints and have his photograph taken. The fingerprints and photograph are checked against a database of known criminals and suspected terrorists. When the traveler arrives in the United States at the port of entry, those same fingerprints and photographs will be used to verify that the person who received the visa is the same person attempting to gain entry. Other potential applications include ATM and check-cashing security. The software is able to quickly verify a customer’s face. After a customer consents, the ATM or check-cashing kiosk captures a digital image of him. The FaceIt software then generates a faceprint of the photograph to protect customers against identity theft and fraudulent transactions. By using the facial recognition software, there’s no need for a picture ID, bankcard or personal identification number (PIN) to verify a customer’s identity. This way businesses can prevent fraud from occurring.

Conclusion:
Face recognition technologies have been associated generally with very costly top secure applications. Today the core technologies have evolced and the cost of equipments is going down dramatically due to the integration and the increasing processing power . certain application of face recognition technologies are now cost effective , reliable and highly accurate. As a result there no technological or financial barriers for stepping from the pilot project to widespread deployment.

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