Prototype of Student Attendance Application Based on Face Recognition Using Eigenface Algorithm

2019 ◽  
Vol 3 (1) ◽  
pp. 23
Author(s):  
Tio Eko Prabowo ◽  
Rudy Hartanto ◽  
Sunu Wibirama
Keyword(s):  
Face Recognition ◽  
Light Intensity ◽  
Recognition Accuracy ◽  
Recognition Algorithm ◽  
Test Results ◽  
Student Attendance ◽  
Environmental Light ◽  
Student Manual ◽  
Face Detection And Recognition ◽  
Axis Rotation

Prototype of face recognition based attendance application that has been developed to overcome weaknesses in DTETI UGM student manual attendance system has several weaknesses. These weaknesses are a decrease in facial recognition accuracy when operating under conditions of varying environmental light intensity and in condition of face rotating towards z axis rotation centre. In addition, application prototype also does not yet have a database to store attendance results. In this paper, a new application prototype has been developed using Eigenface face detection and recognition algorithm and Haar-based Cascade Classifier. Meanwhile, to overcome prototype performance weaknesses of the previously developed application, a pre-processing method was proposed in another study was added. Processes in the method were geometry transformation, histogram levelling separately, image smoothing using bilateral filtering, and elliptical masking. The test results showed that in the category of various environmental light intensity conditions, face recognition accuracy from developed application prototypes was 16.71% better than previous application prototypes. Meanwhile, in category of face slope conditions at z axis rotation centre, face recognition accuracy from developed application prototype was 38.47% better. Attendance database system was also successfully implemented and running without error.

Face Recognition Algorithm Based on 3D Point Cloud Acquired by Mixed Image Sensor

2019 ◽  
Vol 23 (2) ◽  
pp. 366-369
Author(s):  
Tang-Tang Yi ◽  
Keyword(s):  
Face Recognition ◽  
Point Cloud ◽  
Recognition Accuracy ◽  
Image Sensor ◽  
Recognition Algorithm ◽  
Face Image ◽  
3D Point Cloud ◽  
3D Face ◽  
Face Images ◽  
The Face

In order to solve the problem of low recognition accuracy in recognition of 3D face images collected by traditional sensors, a face recognition algorithm for 3D point cloud collected by mixed image sensors is proposed. The algorithm first uses the 3D wheelbase to expand the face image edge. According to the 3D wheelbase, the noise of extended image is detected, and median filtering is used to eliminate the detected noise. Secondly, the priority of the boundary pixels to recognize the face image in the denoising image recognition process is determined, and the key parts such as the illuminance line are analyzed, so that the recognition of the 3D point cloud face image is completed. Experiments show that the proposed algorithm improves the recognition accuracy of 3D face images, which recognition time is lower than that of the traditional algorithm by about 4 times, and the recognition efficiency is high.

scholarly journals An MPCA/LDA Based Dimensionality Reduction Algorithm for Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jun Huang ◽  
Kehua Su ◽  
Jamal El-Den ◽  
Tao Hu ◽  
Junlong Li
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Feature Space ◽  
Recognition Algorithm ◽  
Linear Discriminant ◽  
Face Images ◽  
Sample Set ◽  
Multilinear Principal Component Analysis

We proposed a face recognition algorithm based on both the multilinear principal component analysis (MPCA) and linear discriminant analysis (LDA). Compared with current traditional existing face recognition methods, our approach treats face images as multidimensional tensor in order to find the optimal tensor subspace for accomplishing dimension reduction. The LDA is used to project samples to a new discriminant feature space, while theKnearest neighbor (KNN) is adopted for sample set classification. The results of our study and the developed algorithm are validated with face databases ORL, FERET, and YALE and compared with PCA, MPCA, and PCA + LDA methods, which demonstrates an improvement in face recognition accuracy.

scholarly journals Intruder detection and recognition using different image processing techniques for a proactive surveillance

2021 ◽  
Vol 24 (2) ◽  
pp. 843
Author(s):  
Nelson C. Rodelas ◽  
Melvin A. Ballera
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Research Problem ◽  
Recognition Algorithm ◽  
Evaluation Framework ◽  
Image Processing Techniques ◽  
Face Detection And Recognition ◽  
Processing Techniques ◽  
Detection And Recognition

To innovate a proactive surveillance camera, there is a need for efficient face detection and recognition algorithm. The researchers used one of the ViolaJones algorithm and used different image processing techniques to recognize intruders or not. The goal of the research is to recognize the fastest way on how the homeowners will be informed if an intruder or burglar enters their home using a proactive surveillance device. This device was programmed based on the different recognition algorithms and a criteria evaluation framework that could recognize intruders and burglars and the design used was developmental research to satisfy the research problem. The researchers used the Viola-Jones algorithm for face detection and five algorithms for face recognition. The criteria evaluation was used to identify the best face recognition algorithm and was tested in a real-world situation and captured a series of images camera and processed by proactive face detection and recognition. The result shows that the system can detect and recognize intruders and proactively send a notification to the homeowners via mobile application. It is concluded that the system can recognize the intruders and proactively notify the household members using the mobile applications and activate the alarm system of the house.

scholarly journals Automatic Student Attendance System using Face Recognition

2020 ◽  
Vol 9 (3) ◽  
pp. 93-99
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Automated Systems ◽  
Student Attendance ◽  
Face Recognition System ◽  
The Face ◽  
Face Detection And Recognition ◽  
Class Room ◽  
Current Systems ◽  
Detection And Recognition

The most common difficulty that every teacher faces in class room is to take the attendance of the students one by one in each and every class. For the time being many automated systems has been proposed for taking student attendance. In this paper, I introduced an automated student attendance system based on the use of unique techniques for face detection and recognition. This system automatically detects the student when he or she enters the classroom and recognizes that specific student and marks the student's attendance. This method also focuses on the specific features of different attributes such as the face, eye and nose of humans. In order to evaluate the performance of different face recognition system, different real-time situations are considered. This paper also suggests the technique for handling the technique such as spoofing and avoiding student proxy. This system helps track students compared to traditional or current systems and thereby saves time.

Modified Local Binary Pattern Algorithm for Feature Dimensionality Reduction.

2019 ◽  
Vol 12 ◽  
Author(s):  
Manish Kumar ◽  
Rahul Gupta ◽  
Kota Solomon Raju ◽  
Dinesh Kumar ◽  
Dinesh Kumar
Keyword(s):  
Face Recognition ◽  
Dimensionality Reduction ◽  
Local Binary Pattern ◽  
Similarity Index ◽  
Recognition Algorithm ◽  
Query Image ◽  
Modified Method ◽  
Face Detection And Recognition ◽  
Feature Dimensionality Reduction ◽  
Maximum Similarity

A face recognition algorithm with feature dimensionality reduction is proposed. The proposed algorithm is based on a variant of Local Binary Pattern (LBP) for face detection and recognition. The features of each block of face image is extracted and then global feature of face is constructed from super histogram. For recognition, traditional methods are used. The query image is compared with the dataset (ORL Dataset, LFW Dataset and Yale Dataset) in similarity index and the minimum distance. The maximum similarity is used to define as the class of query image. The reduction in number of features is achieved by modifying the traditional LBP process. The proposed modified method is observed as more fast and efficient for face recognition as compared to the existing algorithms.

scholarly journals A Discriminative Target Equation-Based Face Recognition Method for Teaching Attendance

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Feng Xu ◽  
Haiwei Wang
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Face Detection ◽  
Recognition Accuracy ◽  
Test Sample ◽  
Histogram Equalization ◽  
Training Sample ◽  
Reconstruction Error ◽  
Student Attendance ◽  
The Face

In this paper, we use discriminative objective equations to conduct an in-depth study and analysis of face recognition methods in teaching attendance and use the model in actual teaching attendance. It focuses on the design and implementation of the attendance module, which uses wireless network technology to record students’ access to classrooms in real time, and relies on face recognition technology to identify students’ sign-in images to achieve attendance records of students’ independent attendance sign-in. Real-time detection of student attendance is achieved by combining face detection and face recognition technology through regular camera photography and automatic attendance check-in by the server. Based on the recognition results of the attendance check-in image, an attendance mechanism is proposed, and the attendance score of the student for the current course can be calculated using the attendance mechanism, which realizes the automatic management of student attendance. For the face recognition process, the system uses the Ad boost algorithm based on Hear features to achieve face detection, preprocesses the face samples with gray normalization, rotation correction, and size correction, and uses the method based on LBP features to achieve face recognition. Firstly, a combination of histogram equalization and wavelet denoising is chosen to preprocess the training sample images to obtain the face image light invariance description, and then, the initial dictionary is constructed using the dimensionality reduction performance of the PCA method; next, the initial dictionary is updated, and a new dictionary with representation and discrimination capabilities is obtained using the LC-KSVD algorithm that makes improvements in the dictionary update stage. The sparse coefficients of the feature matrix of the test sample image under the new dictionary are calculated, and the class correlation reconstruction is performed on the feature matrix of the test sample image, and the corresponding reconstruction error is solved; finally, the discriminative classification of the test sample image is achieved according to the solved class correlation reconstruction error. The relevant experiments on the face database prove that the algorithm can improve the recognition accuracy to a certain extent and better solve the influence of changing lighting conditions on the face recognition accuracy.

scholarly journals Automatic Attendance System with Face Recognition using Machine Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 3642-3646
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Recognition Algorithm ◽  
Machine Learning Techniques ◽  
Project Work ◽  
Data Set ◽  
Second Stage ◽  
Learning Techniques ◽  
The Face ◽  
Face Detection And Recognition

Object and Face detection and recognition is one of the mostly researched area in computer vision. This particular field of work is widely used in mobile phones and laptops for unlocking the system by the user. Recently this field gained importance in the automatic attendance system in schools, colleges and institution. The institutions are moving from biometric based attendance to face recognition based attendance system. In this project work, I have used machine learning techniques to create a complete system of automatic attendance system which can be implemented very easily. There are majorly four steps involved in the system. Firstly, the datasets can be created instantly using webcam and in the second stage the created data set have to be trained and the trainer algorithm will create the trainer.yml document. As a next step, the face recognition algorithm have to be performed in order to recognize the faces of various students and teacher. In the final step, the attendance of the students will be updated in the CSV file or Excel. The proposed work is very much suited for the real time applications like automatic attendance system. HaarCascade is very eff

Fine structure of the retina of the giant scallop, Placopecten magellanicus

1984 ◽  
Vol 42 ◽  
pp. 312-313
Author(s):  
C.V.L. Powell
Keyword(s):  
Fine Structure ◽  
Electron Microscope ◽  
Light Intensity ◽  
Scanning Electron Microscope ◽  
Eye Development ◽  
Preliminary Observation ◽  
Columnar Epithelium ◽  
Placopecten Magellanicus ◽  
Environmental Light ◽  
Scanning Electron

The overall fine structure of the eye in Placopecten is similar to that of other scallops. The optic tentacle consists of an outer columnar epithelium which is modified into a pigmented iris and a cornea (Fig. 1). This capsule encloses the cellular lens, retina, reflecting argentea and the pigmented tapetum. The retina is divided into two parts (Fig. 2). The distal retina functions in the detection of movement and the proximal retina monitors environmental light intensity. The purpose of the present study is to describe the ultrastructure of the retina as a preliminary observation on eye development. This is also the first known presentation of scanning electron microscope studies of the eye of the scallop.

Hybrid Face Recognition Algorithm for Wireless Sensor Network

2017 ◽  
Vol 13 (3) ◽  
pp. 267-281
Author(s):  
Matheel E. Abdulmunem E. Abdulmunem ◽  
◽  
Fatima B. Ibrahim
Keyword(s):  
Face Recognition ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Recognition Algorithm ◽  
Wireless Sensor
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