FACE DETECTION AND RECOGNITION USING MAXIMUM LIKELIHOOD CLASSIFIERS ON GABOR GRAPHS

2009 ◽  
Vol 23 (03) ◽  
pp. 433-461 ◽  
Author(s):  
MANUEL GÜNTHER ◽  
ROLF P. WÜRTZ
Keyword(s):  
Face Recognition ◽  
Maximum Likelihood ◽  
Face Detection ◽  
Recognition System ◽  
Similarity Functions ◽  
Expression Variation ◽  
Face Recognition System ◽  
Face Detection And Recognition ◽  
Ml Estimator ◽  
Detection And Recognition

We present an integrated face recognition system that combines a Maximum Likelihood (ML) estimator with Gabor graphs for face detection under varying scale and in-plane rotation and matching as well as a Bayesian intrapersonal/extrapersonal classifier (BIC) on graph similarities for face recognition. We have tested a variety of similarity functions and achieved verification rates (at FAR 0.1%) of 90.5% on expression-variation and 95.8% on size-varying frontal images within the CAS-PEAL database. Performing Experiment 1 of FRGC ver2.0, the method achieved a verification rate of 72%.

scholarly journals Challenges in Face Detection and Recognition techniques

2020 ◽  
Vol 9 (8) ◽  
pp. 221-224
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Recognition System ◽  
Research Paper ◽  
Facial Features ◽  
Image Information ◽  
Public Crisis ◽  
Face Recognition System ◽  
Face Detection And Recognition ◽  
Detection And Recognition

Face Recognition System is popular topic in the biometric world .This system provide Features to detect the person’s face and identify on basis of existing records in database .The aim of this study is to described how to show various facial features of an image. Face Recognition system, based on Biometric AI, uniquely finds out a person by analyzing the person's facial textures and shape. In this paper, our aim is to study various face detect and recognition techniques such as Harr Like Feature Algorithm resulting to retort criminality and public crisis. Also, some facial recognition approaches PCA and LDA have been discussed in the research paper for abstracting the image information.

A Real-Time Face Recognition System for Android Smart Phone

2013 ◽  
Vol 756-759 ◽  
pp. 4006-4010 ◽  
Author(s):  
Gang Yang ◽  
Jia Ni Luo
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Face Detection ◽  
Smart Phone ◽  
Recognition System ◽  
Adaboost Algorithm ◽  
Face Recognition System ◽  
Android Platform ◽  
Face Detection And Recognition ◽  
Detection And Recognition

With the widely application of face recognition and the rapid development of Android OS, technique of face detection and recognition based on Android platform becomes increasingly attractive. This paper presents a real-time face recognition system on Android platform. The system realizes face detection by applying AdaBoost algorithm and face recognition by utilizing Eigenfaces. This paper also came up with some methods to speed up the face detection and recognition process and improve the correct rate of face recognition. Experimental results show that this system is able to realize real-time face detection and recognition on Android smart phones. In addition, all the work is completed on the smart phone without using any other terminals or tools.

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.

scholarly journals A Face Recognition Method in the Internet of Things for Security in Smart Recognition Places

2022 ◽  
Vol 10 (1) ◽  
pp. 687-690
Author(s):  
Prof. Kalpana Malpe
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Associate Degree ◽  
Human Life ◽  
Recognition System ◽  
Raspberry Pi ◽  
Living Things ◽  
Face Recognition System ◽  
The Face ◽  
Face Detection And Recognition

Abstract: In recent years, the safety constitutes the foremost necessary section of the human life. At this point, the price is that the greatest issue. This technique is incredibly helpful for reducing the price of watching the movement from outside. During this paper, a period of time recognition system is planned which will equip for handling pictures terribly quickly. The most objective of this paper is to safeguard home, workplace by recognizing individuals. The face is that the foremost distinctivea part of human’s body. So, it will replicate several emotions of associate degree Expression. A few years past, humans were mistreatment the non-living things like good cards, plastic cards, PINS, tokens and keys for authentication, and to urge grant access in restricted areas like ISRO, National Aeronautics and Space Administration and DRDO. The most necessary options of the face image are Eyes, Nose and mouth. Face detection and recognition system is simpler, cheaper, a lot of accurate, process. The system under two categories one is face detection and face recognition. Throughout this case, among the paper, the Raspberry Pi single-board computer is also a heart of the embedded face recognition system. Keywords: Raspberry Pi, Face recognition system

Research and Implementation of Face Detection Based on OpenCV

2014 ◽  
Vol 971-973 ◽  
pp. 1710-1713
Author(s):  
Wen Huan Wu ◽  
Ying Jun Zhao ◽  
Yong Fei Che
Keyword(s):  
Face Recognition ◽  
Detailed Analysis ◽  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Accuracy Rate ◽  
Face Recognition System ◽  
The Face

Face detection is the key point in automatic face recognition system. This paper introduces the face detection algorithm with a cascade of Adaboost classifiers and how to configure OpenCV in MCVS. Using OpenCV realized the face detection. And a detailed analysis of the face detection results is presented. Through experiment, we found that the method used in this article has a high accuracy rate and better real-time.

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 Implementation of Face Recognition System on Raspberry Pi

2018 ◽  
Vol 7 (2.17) ◽  
pp. 85
Author(s):  
K Raju ◽  
Dr Y.Srinivasa Rao
Keyword(s):  
Face Recognition ◽  
High Performance ◽  
Recognition Rate ◽  
Cost Effective ◽  
Recognition System ◽  
Raspberry Pi ◽  
Face Recognition System ◽  
Effective System ◽  
Face Detection And Recognition ◽  
Improved Accuracy

Face Recognition is the ability to find and detect a person by their facial attributes. Face is a multi dimensional and thus requires a considerable measure of scientific calculations. Face recognition system is very useful and important for security, law authorization applications, client confirmation and so forth. Hence there is a need for an efficient and cost effective system. There are numerous techniques that are as of now proposed with low Recognition rate and high false alarm rate. Hence the major task of the research is to develop face recognition system with improved accuracy and improved recognition time. Our objective is to implementing Raspberry Pi based face recognition system using conventional face detection and recognition techniques such as A Haar cascade classifier is trained for detection and Local Binary Pattern (LBP) as a feature extraction technique. With the use of the Raspberry Pi kit, we go for influencing the framework with less cost and simple to use, with high performance. 

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