Remote sensing training for Corps of Engineering personnel: The university training module concept

A concept to permit Corps of Engineers personnel to obtain and maintain an appropriate level of individual proficiency in the application of remote sensing to water resource management is described. Recommendations are made for specific training courses and include structure and staffing requirements, syllabi and methods of operation, supporting materials, and procedures for integrating information systems management into the University Training Modules

Automatic Attendance Using Face Recognition

Human face detection and recognition is an important technology used in various applications such as video monitor system. Traditional method for taking attendance is Roll Number of student and record the attendance in sheet which takes a lot of time. Because of that systems like automatic attendance is used. To overcome the problems like wastage of time, incorrect attendance, the proposed system gives a method like when he enters the class room , system marks the attendance by extracting the image using Principal Component Analysis algorithm. The system will record the attendance of the student automatically. The student database is collected, it includes name of the students, there images and roll number. It carries an entry in log report of every student of each subject and generates a pdf report of the attendance of the student

A Bimodal Biometric Student Attendance System

A lot of attempts have been made to use biometrics in class attendance systems. Most of the implemented biometric attendance systems are unimodal. Unimodal biometric systems may be spoofed easily, leading to a reduction in recognition accuracy. This paper explores the use of bimodal biometrics to improve the recognition accuracy of automated student attendance systems. The system uses the face and fingerprint to take students’ attendance. The students’ faces were captured using webcam and preprocessed by converting the color images to grey scale images. The grey scale images were then normalized to reduce noise. Principal Component Analysis (PCA) algorithm was used for facial feature extraction while Support Vector Machine (SVM) was used for classification. Fingerprints were captured using a fingerprint reader. A thinning algorithm digitized and extracted the minutiae from the scanned fingerprints. The logical technique (OR) was used to fuse the two biometric data at the decision level. The fingerprint templates and facial images of each user were stored along with their particulars in a database. The implemented system had a minimum recognition accuracy of 87.83%

Student Attendance System Based on Fingerprint Recognition and One to Many Matching

Our project aims at designing an student attendance system which could effectively manage attendance of students at institutes like NIT Rourkela. Attendance is marked after student identification. For student identification, a fingerprint recognition based identification system is used. Fingerprints are considered to be the best and fastest method for biometric identification. They are secure to use, unique for every person and does not change in one's lifetime. Fingerprint recognition is a mature field today, but still identifying individual from a set of enrolled fingerprints is a time taking process. It was our responsibility to improve the fingerprint identification system for implementation on large databases e.g. of an institute or a country etc. In this project, many new algorithms have been used e.g. gender estimation, key based one to many matching, removing boundary minutiae. Using these new algorithms, we have developed an identification system which is faster in implementation than any other available today in the market. Although we are using this fingerprint identification system for student identification purpose in our project, the matching results are so good that it could perform very well on large databases like that of a country like India (MNIC Project). This system was implemented in Matlab10, Intel Core2Duo processor and comparison of our one to many identification was done with existing identification technique i.e. one to one identification on same platform. Our matching technique runs in O(n+N) time as compared to the existing O(Nn^2). The fingerprint identification system was tested on FVC2004 and Verifinger databases