Eigenface

About: Eigenface is a research topic. Over the lifetime, 2128 publications have been published within this topic receiving 110119 citations.

Real-Time Smart Attendance System using Face Recognition Techniques

Abstract: The management of the attendance can be a great burden on the teachers if it is done by hand. To resolve this problem, smart and auto attendance management system is being utilized. But authentication is an important issue in this system. The smart attendance system is generally executed with the help of biometrics. Face recognition is one of the biometric methods to improve this system. Being a prime feature of biometric verification, facial recognition is being used enormously in several such applications, like video monitoring and CCTV footage system, an interaction between computer & humans and access systems present indoors and network security. By utilizing this framework, the problem of proxies and students being marked present even though they are not physically present can easily be solved. The main implementation steps used in this type of system are face detection and recognizing the detected face.This paper proposes a model for implementing an automated attendance management system for students of a class by making use of face recognition technique, by using Eigenface values, Principle Component Analysis (PCA) and Convolutional Neural Network (CNN). After these, the connection of recognized faces ought to be conceivable by comparing with the database containing student's faces. This model will be a successful technique to manage the attendance and records of students.

Interactive Artificial Bee Colony Supported Passive Continuous Authentication System

Abstract: In this paper, an initiative passive continuous authentication (CA) system based on both hard and soft biometrics is presented. Human facial features are used as hard biometric information for the authentication process, and the clothes' color of a user is employed as the soft biometric information. The passive CA system keeps verifying, without interrupting the user from concentrating on his work. It also provides the capacity for the machine to recognize who is in front of the terminal, reduces the potential security leaks, and denies access to the invader with the stolen account and password. In this system, the face recognition core is implemented not only by the Eigenface method, but also assisted by the interactive artificial bee colony optimization algorithm. The proposed method is evaluated by the ORL face database and tested on the prototype CA system for computer security. The experimental results indicate that the accuracy of recognition is raised up to 3.13%, i.e., from 83.75% to 86.88%, with data from the ORL database, and it is improved by 34.53% on average in the real-time continuous authentication environment.

Antifaces: a novel, fast method for image detection

Abstract: This paper offers a novel detection method, which works well even in the case of a complicated image collection. It can also be applied to detect 3D objects under different views. The detection problem is solved by sequentially applying very simple filters (or detectors), which are designed to yield small results on the multitemplate (hence antifaces), and large results on "random" natural images. This is achieved by making use of a simple probabilistic assumption on the distribution of natural images, which is borne out well in practice. Only images which passed the threshold test imposed by the first detector are examined by the second detector, etc. The detectors are designed to act independently so that their false alarms are uncorrelated; this results in a false alarm rate which decreases exponentially in the number of detectors. The algorithm's performance compares favorably to the well-known eigenface and support vector machine based algorithms, but is substantially faster.

Expression-Invariant Face Recognition with Expression Classification

Abstract: Face recognition is one of the most intensively studied topics in computer vision and pattern recognition. Facial expression, which changes face geometry, usually has an adverse effect on the performance of a face recognition system. On the other hand, face geometry is a useful cue for recognition. Taking these into account, we utilize the idea of separating geometry and texture information in a face image and model the two types of information by projecting them into separate PCA spaces which are specially designed to capture the distinctive features among different individuals. Subsequently, the texture and geometry attributes are re-combined to form a classifier which is capable of recognizing faces with different expressions. Finally, by studying face geometry, we are able to determine which type of facial expression has been carried out, thus build an expression classifier. Numerical validations of the proposed method are given.