13-02-2013, 04:43 PM
Face Recognition-based Lecture Attendance System
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Abstract
In this paper, we propose a system that takes the attendance of students for classroom lecture. Our system takes the
attendance automatically using face recognition. However, it is difficult to estimate the attendance precisely using
each result of face recognition independently because the face detection rate is not sufficiently high. In this paper,
we propose a method for estimating the attendance precisely using all the results of face recognition obtained by
continuous observation. Continuous observation improves the performance for the estimation of the attendance We
constructed the lecture attendance system based on face recognition, and applied the system to classroom lecture. This
paper first review the related works in the field of attendance management and face recognition. Then, it introduces
our system structure and plan. Finally, experiments are implemented to provide as evidence to support our plan. The
result shows that continuous observation improved the performance for the estimation of the attendance.
Introduction
Though the video streaming service of lecture archive
is readily available in many systems, students have few
opportunities to view the lecture in this service because
lecture content is not summarized. If the attendance of
a student of classroom lecture is attached to the video
streaming service, it is possible to present the video of
the time when he was absent. It is important to take the
attendance of the students in the classroom automatically.
ID tag or other identifications such the record of login/
out in most e-Learning systems are not sufficient because
it does not represent students’ context in face-toface
classroom. It is also difficult to grasp the contexts
by the data of a single moment.
student’s context such as presence, seat position, status,
and comprehension are discussed in this paper. At
the same time face images reflect a lot about these context
information. It is possible to estimate automatically
whether each student is present or absent and where each
student is sitting by using face recognition technology. It
is also possible to know whether students are awake or
sleeping and whether students are interested or bored in
lecture if face images are annotated with the students’
name, the time and the place. We are concerned with
the method to use face image processing technology.
By continuously observing of face information, our approach
can solve low effectiveness of existing face detection
technology, and improve the accuracy of face recognition.
We propose a method that take the attendance using
face recognition based on continuous observation. In this
paper, our purpose is to obtain the attendance, positions
and images of students’ face, which are useful information
in the classroom lecture.
Related work
Cheng, et al. [1] developed the system to manage the
context of the students for the classroom lecture by using
note PCs for all the students. Because this system uses
the note PC of each student, the attendance and the
position of the students are obtained. However, it is
difficult to know the detailed situation of the lecture.
our system takes images of faces.
In recent decade, a number of algorithms for face
recognition have been proposed [2], but most of these
works deal with only single image of a face at a time. By
continuously observing of face information, our approach
can solve the problem of the face detection, and improve
the accuracy of face recognition.
Estimating students’ existence
We use the method of ASD to estimate the existence of
a student sitting on the seat. It is described in detail in
[3]. In this approach, an observation camera with fisheye
lens is installed on the ceiling of the classroom and
looks down at the student area vertically. ASD estimates
students’ existence by using the background subtraction
and inter-frame subtraction of the images captured by
the sensing camera (see Figure 2). In the background
subtraction method, noise factors like bags and coats of
the students are also detected, and the students are not
detected if the color of clothes of them are similar the
seats. ASD makes use of the inter-frame subtraction to
detect the moving of the students.
Shooting plan
Camera planning module selects one seat from the estimated
sitting area in order to determine where to direct
the front camera. Actually, in this paper, the module selects
a seat by scanning the seats sequentially. This approach
is insufficient because it wastes time directing the
camera to where the student-and-seat the seats the students
correspondence is already decided In other words,
if we direct the camera to each seat with the same probability,
it is difficult to detect the faces according to the
student or the seat, and the system judges the students
who are actually present to be absent consequently. In
order to solve this problem, it is important to the information
of each student’s position.
The camera is directed to the selected seat using the
pan/tilt/zoom that have been registered in the database.
The camera captures the image of the student.