scholarly journals Identification and Recognition of facial images

2020 ◽  
pp. 69-76
Author(s):  
Mallika Kohli ◽  
Vasundra Wazir ◽  
Parul Sharma ◽  
Pawanesh Abrol
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Main Task ◽  
Face Recognition System ◽  
The Face ◽  
Almost All ◽  
Facial Images

Face detection is the power to identify a face and recognition is the ability to recognize whose face it is by means of facial characteristics. Face is multivariate and requires a lot of mathematical summation. Almost all imperative applications use a face recognition system. There are many methods that have been already proposed which provides low recognition rate. Hence, the main task of research is to develop a face recognition system with higher recognition capability and better accuracy. This paper proposes Face recognition system by combining two techniques Viola Jones and Principal Component Analysis. An approach of Eigen faces is employed in Principle Component Analysis(PCA). The face recognition system is implemented in MATLAB.

A Robust and Efficient Face Recognition System Based on Luminance Distribution Using Maximum Likelihood

2012 ◽  
Vol 241-244 ◽  
pp. 1705-1709
Author(s):  
Ching Tang Hsieh ◽  
Chia Shing Hu
Keyword(s):  
Face Recognition ◽  
Maximum Likelihood ◽  
Recognition Rate ◽  
Computation Time ◽  
Likelihood Estimation ◽  
Recognition System ◽  
Luminance Distribution ◽  
Face Region ◽  
Face Recognition System ◽  
The Face

In this paper, a robust and efficient face recognition system based on luminance distribution by using maximum likelihood estimation is proposed. The distribution of luminance components of the face region is acquired and applied to maximum likelihood test for face matching. The experimental results showed that the proposed method has a high recognition rate and requires less computation time.

scholarly journals Real-Time Attendance and Feedback System

2021 ◽  
pp. 1135-1139
Author(s):  
Piyush Manish Sonar ◽  
Aniket Nitin Chaudhari ◽  
Mehul Deepak Sethi ◽  
Tejaswini Sanjay Gadakh
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Feedback System ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Student Attendance ◽  
Feedback Form ◽  
Face Recognition System ◽  
User Friendly ◽  
Facial Images

Face is the representation of one’s identity. Hence, we have proposed an automated student attendance system based on face recognition. Face recognition system is very useful in life applications especially for attendance system. In our proposed approach, firstly, video framing is performed by activating the camera through a user-friendly interface. In the pre-processing stage, scaling of the size of images is performed, if necessary, in order to prevent loss of information. In face recognition stage, enhanced local binary pattern (LBP) and principal component analysis (PCA) is applied correspondingly in order to extract the features from facial images. Another way of marking the attendance is fingerprint recognition. To mark the attendance students simply have to give the fingerprint impression in fingerprint scanner module. Finally, the attendance of the recognized student will be marked and saved in the excel file. The student who is not registered will also be able to register on the spot and notification will be given if students sign in more than once. Whenever seminar is completed then a link is sent on email. It includes the information in terms of feedback. When student fills the feedback form then analysis of overall session is done.

Robust Face Recognition Technique for a Real-Time Embedded Face Recognition System

2008 ◽  
pp. 188-211
Author(s):  
Ting Shan ◽  
Abbas Bigdeli ◽  
Brian C. Lovell ◽  
Shaokang Chen
Keyword(s):  
Face Recognition ◽  
Embedded System ◽  
Principal Component ◽  
Recognition System ◽  
Active Appearance Models ◽  
Face Recognition System ◽  
The Face ◽  
The Embedded System ◽  
Computationally Intensive ◽  
Robust Face Recognition

In this chapter, we propose a pose variability compensation technique, which synthesizes realistic frontal face images from nonfrontal views. It is based on modeling the face via active appearance models and estimating the pose through a correlation model. The proposed technique is coupled with adaptive principal component analysis (APCA), which was previously shown to perform well in the presence of both lighting and expression variations. The proposed recognition techniques, though advanced, are not computationally intensive. So they are quite well suited to the embedded system environment. Indeed, the authors have implemented an early prototype of a face recognition module on a mobile camera phone so the camera can be used to identify the person holding the phone.

Development of Class Attendance System Using Face Recognition for Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia

2020 ◽  
pp. 1-42
Author(s):  
Pauline Ong ◽  
Tze Wei Chong ◽  
Woon Kiow Lee
Keyword(s):  
Face Recognition ◽  
Traditional Approach ◽  
Principal Component ◽  
Recognition System ◽  
Support Vector ◽  
Original Image ◽  
Student Attendance ◽  
Biometric Verification ◽  
Face Recognition System ◽  
The Face

The traditional approach of student attendance monitoring system in Universiti Tun Hussein Onn Malaysia is slow and disruptive. As a solution, biometric verification based on face recognition for student attendance monitoring was presented. The face recognition system consisted of five main stages. Firstly, face images under various conditions were acquired. Next, face detection was performed using the Viola Jones algorithm to detect the face in the original image. The original image was minimized and transformed into grayscale for faster computation. Histogram techniques of oriented gradients was applied to extract the features from the grayscale images, followed by the principal component analysis (PCA) in dimension reduction stage. Face recognition, the last stage of the entire system, using support vector machine (SVM) as classifier. The development of a graphical user interface for student attendance monitoring was also involved. The highest face recognition accuracy of 62% was achieved. The obtained results are less promising which warrants further analysis and improvement.

scholarly journals Comparison of principal component analysis algorithm and local binary pattern for feature extraction on face recognition system

2018 ◽  
Vol 197 ◽  
pp. 03001
Author(s):  
Ichsan Taufik ◽  
Maya Musthopa ◽  
Aldy Rialdy Atmadja ◽  
Muhammad Ali Ramdhani ◽  
Yana Aditia Gerhana ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Recognition Time ◽  
Characteristic Extraction ◽  
Face Recognition System ◽  
Sample Test

Characteristic extraction in face recognition is a step to get characteristic information from the image. The characteristic extraction algorithm is tested against several scenarios of different sunlight and lights, objects facing the camera and not facing the camera. The sample test data were performed on 4 people using a video file or frame numbering 70 for recognizable faces using Principal Component Analysis (PCA) and Local Binary Pattern (LBP) algorithms. The result of the research shows that Local Binary Pattern (LBP) algorithm in object scenario facing camera with sunlighting in room has accuracy of 98.59%, recognition time of 812,817 milliseconds, FAR of 1,41% and FRR of 0%, while at Principal Component Analysis (PCA) 98.59% accuracy, recognition time of 1275,761 milliseconds, FAR of 1.41% and FRR of 0%. Based on these results, the Local Binary Pattern (LBP) algorithm is more efficient than Principal Component Analysis (PCA) for face recognition of the scenarios to be implemented in real-time video.

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