Student Attendance System in Crowded Classrooms Using a Smartphone Camera

Mery, Domingo; Mackenney, Ignacio; Villalobos, Esteban

doi:10.1109/wacv.2019.00096

Cited by 33 publications

(11 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RELATED WORK "Student Attendance System in Crowded Classrooms Using a Smart phone Camera" (Domingo Mery, Ignacio Mackenney and Esteban Villalobos) [21], For busy schools, manually managing the attendance sheets is time-consuming. The automatic student attendance system that may be employed in crowded classrooms and in which the session photos are captured by a smartphone camera is proposed and evaluated in this research.…”

Section: IImentioning

confidence: 99%

Enhanced Technique for Multiple Face Detection Based on LBPH (Local Binary Patterns Histograms): A Review

Kumar¹,

Shukla²

2022

IJRASET

View full text Add to dashboard Cite

Facial recognition has long been an active study area because of its lack of modelling requirements and the wide range of applications. Because of this, daily tasks are increasingly being completed electronically rather than with pen and paper. Currently, computer vision is a broad topic that deals with complex programming by using input images and videos to carry out automatic tasks including detection, recognition, and classification. Face recognition and its applications have recently been regarded as one of the most successful image analysis applications, especially over the past few years. Even deep learning approaches, they are superior to the typical human visual system. Face Recognition is a special technology that uses distinctive facial traits to identify or confirm a person

show abstract

Section: IImentioning

confidence: 99%

Enhanced Technique for Multiple Face Detection Based on LBPH (Local Binary Patterns Histograms): A Review

Kumar¹,

Shukla²

2022

IJRASET

View full text Add to dashboard Cite

show abstract

“…By using an eye contact detection method, they quantify the highly dynamic nature of everyday visual attention across users, mobile applications, and usage contexts. Mery et al [33] in 2019, evaluated a general methodology for the automated student attendance system that can be used in crowded classrooms, in which the session images are taken by a smartphone camera. Copeland and Gedeon [34] investigated eye tracking on how different sequences of text and test questions affect performance outcomes, eye movements, and reading behavior for first (L1) English language and second (L2) English language readers.…”

Section: Introductionmentioning

confidence: 99%

“…Some of the reported research capture video, but there is not an analysis of the obtained information. Mery et al [33] proposed an automated student attendance system that can be used in crowded classrooms, in which the session images are taken by a smartphone camera. Chern et al [19] implemented a smartphone-based hearing system that enhances the listening experience in a learning environment, including: students with hearing loss, students with ADHD or students in a foreign language classroom.…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Based Remote Monitoring Tool for e-Learning

et al. 2020

View full text Add to dashboard Cite

In this paper, a smartphone-based learning monitoring system is presented. During pandemics, most of the parents are not used to simultaneously deal with their home office activities and the monitoring of the home school activities of their children. Therefore, a system allowing a parent, teacher or tutor to assign a task and its corresponding execution time to children, could be helpful in this situation. In this work, a mobile application to assign academic tasks to a child, measure execution time, and monitor the child's attention, is proposed. The children are the users of a mobile application, hosted on a smartphone or tablet device, that displays an assigned task and keeps track of the time consumed by the child to perform this task. Time measurement is performed using face recognition, so it is possible to infer the attention of the child based on the presence or absence of a face. The app also measures the time that the application was in the foreground, as well as the time that the application was sent to the background, to measure boredom. The parent or teacher assigns a task using a desktop application specifically designed for this purpose. At the end of the time set by the user, the application sends to the parent or teacher statistics about the execution time of the task and the degree of attention of the child.

show abstract

“…In recent years, we have witnessed tremendous improvements in face recognition by using complex deep neural network architectures trained with millions of face images (see for example advances in face recognition [2] [3] [4] and in face detection [5] [6]). In addition, there are very impressive advances in applications [7], face clustering [8] [9], and in the recognition of age [10], gender [11], facial expressions [12], eye gaze [13], head pose [14] and facial landmarks [15].…”

Section: Introductionmentioning

confidence: 99%

Graph Representation for Face Analysis in Image Collections

Mery,

Valdes

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Given an image collection of a social event with a huge number of pictures, it is very useful to have tools that can be used to analyze how the individuals -that are present in the collection-interact with each other. In this paper, we propose an optimal graph representation that is based on the 'connectivity' of them. The connectivity of a pair of subjects gives a score that represents how 'connected' they are. It is estimated based on co-occurrence, closeness, facial expressions, and the orientation of the head when they are looking to each other. In our proposed graph, the nodes represent the subjects of the collection, and the edges correspond to their connectivities. The location of the nodes is estimated according to their connectivity (the closer the nodes, the more connected are the subjects). Finally, we developed a graphical user interface in which we can click onto the nodes (or the edges) to display the corresponding images of the collection in which the subject of the nodes (or the connected subjects) are present. We present relevant results by analyzing a wedding celebration, a sitcom video, a volleyball game and images extracted from Twitter given a hashtag. We believe that this tool can be very helpful to detect the existing social relations in an image collection.

show abstract

Student Attendance System in Crowded Classrooms Using a Smartphone Camera

Cited by 33 publications

References 48 publications

Enhanced Technique for Multiple Face Detection Based on LBPH (Local Binary Patterns Histograms): A Review

Enhanced Technique for Multiple Face Detection Based on LBPH (Local Binary Patterns Histograms): A Review

Smartphone-Based Remote Monitoring Tool for e-Learning

Graph Representation for Face Analysis in Image Collections

Contact Info

Product

Resources

About