Pose estimation and frontal face detection for face recognition

2005 ◽  
Author(s):  
Eng Thiam Lim ◽  
Jiangang Wang ◽  
Wei Xie ◽  
Venkarteswarlu Ronda
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Pose Estimation

The Adaboost Algorithm Applied in ATM Automatic Identification System

2013 ◽  
Vol 753-755 ◽  
pp. 2941-2944
Author(s):  
Ming Hui Zhang ◽  
Yao Yu Zhang
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Computing Time ◽  
Automatic Identification ◽  
Identification System ◽  
Human Face ◽  
Detection Experiment ◽  
Adaboost Algorithm ◽  
Face Features ◽  
Human Faces

Seeing that human face features are unique, an increasing number of face recognition algorithms on existing ATM are proposed. Since face detection is a primary link of face recognition, our system adopts AdaBoost algorithm which is based on face detection. Experiment results demonstrated that the computing time of face detection using this algorithm is about 70ms, and the single and multiple human faces can be effectively measured under well environment, which meets the demand of the system.

scholarly journals Pre filtering techniques for face recognition based on edge detection algorithm

2017 ◽  
Vol 7 (1.1) ◽  
pp. 213
Author(s):  
Sheela Rani ◽  
Vuyyuru Tejaswi ◽  
Bonthu Rohitha ◽  
Bhimavarapu Akhil
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Detection Algorithm ◽  
Facial Features ◽  
Human Face ◽  
Video Frames ◽  
Similar Idea ◽  
The Individual ◽  
The Given ◽  
Interesting Area

Recognition of face has been turned out to be the most important and interesting area in research. A face recognition framework is a PC application that is apt for recognizing or confirming the presence of human face from a computerized picture, from the video frames etc. One of the approaches to do this is by matching the chosen facial features with the pictures in the database. It is normally utilized as a part of security frameworks and can be implemented in different biometrics, for example, unique finger impression or eye iris acknowledgment frameworks. A picture is a mix of edges. The curved line potions where the brightness of the image change intensely are known as edges. We utilize a similar idea in the field of face-detection, the force of facial colours are utilized as a consistent value. Face recognition includes examination of a picture with a database of stored faces keeping in mind the end goal to recognize the individual in the given input picture. The entire procedure covers in three phases face detection, feature extraction and recognition and different strategies are required according to the specified requirements.

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.

scholarly journals Dynamic & Intelligent Student Attendance Maintenance Based on Face Recognition

2020 ◽  
Vol 9 (5) ◽  
pp. 399-403
Keyword(s):  
Artificial Intelligence ◽  
Face Recognition ◽  
Face Detection ◽  
Student Attendance ◽  
Teachers And Students ◽  
The Face ◽  
Monitoring Camera ◽  
On Line

The proposed system generally results a solution to some of the problems which occurs in colleges and schools by providing a monitoring camera with the help of “Artificial Intelligence (AI)” . The main problem which can be occurred is wastage of time in taking the attendance manually or through any biometric sensors. The next problem which can be solved is to control the usage of electricity in classrooms when students are not in class. When the videos are getting recorded with the help of monitoring cameras, at the same time the head counting and face detection of the students present will also be done. When the strength of the class is zero ,the head counting also results to zero. The electricity can also be saved at the same time when people are not present in the classroom. The face recognition is the easiest process which can be done for marking the attendance, where the attendance is marked automatically. This process also helps to prevent the fake attendance. Face recognition and detection is generally based on line edge mapping to attain the identity of the student and also meets the wants of attendance in the universities and schools. The image of the student is to be captured and checked with the database simultaneously and marks the attendance of the particular student. The video gets recorded all the time and checks whether the student remains in class for the entire period.The attendance marking system with the help of technology is very essential for both the teachers and students.

A Few Approaches To Face Detection In Face Recognition Systems

2002 ◽  
pp. 313-322
Author(s):  
Georgi Koukharev ◽  
Tomasz Ponikowski ◽  
Liming Chen
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Recognition Systems

Utilizing Cloud Capabilities for Face Detection and Face Recognition During COVID-19: Comparative Analysis

2021 ◽  
Author(s):  
Vaidehi Fulzele ◽  
Purva Kirad ◽  
Chanchal Dubey ◽  
Yash Kulkarni ◽  
Surabhi Thatte
Keyword(s):  
Face Recognition ◽  
Comparative Analysis ◽  
Face Detection

scholarly journals DTFA-Net: Dynamic and Texture Features Fusion Attention Network for Face Antispoofing

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xin Cheng ◽  
Hongfei Wang ◽  
Jingmei Zhou ◽  
Hui Chang ◽  
Xiangmo Zhao ◽  
...  
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Feature Fusion ◽  
Texture Features ◽  
Replay Attack ◽  
Attention Network ◽  
Detection Failure ◽  
The Face ◽  
Recognition Systems ◽  
Common Face

For face recognition systems, liveness detection can effectively avoid illegal fraud and improve the safety of face recognition systems. Common face attacks include photo printing and video replay attacks. This paper studied the differences between photos, videos, and real faces in static texture and motion information and proposed a living detection structure based on feature fusion and attention mechanism, Dynamic and Texture Fusion Attention Network (DTFA-Net). We proposed a dynamic information fusion structure of an interchannel attention block to fuse the magnitude and direction of optical flow to extract facial motion features. In addition, for the face detection failure of HOG algorithm under complex illumination, we proposed an improved Gamma image preprocessing algorithm, which effectively improved the face detection ability. We conducted experiments on the CASIA-MFSD and Replay Attack Databases. According to experiments, the DTFA-Net proposed in this paper achieved 6.9% EER on CASIA and 2.2% HTER on Replay Attack that was comparable to other methods.

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