scholarly journals PENERAPAN FACE RECOGNITION DENGAN METODE EIGENFACE PADA INTELIGENT CAR SECURITY

2017 ◽  
Author(s):  
SEHMAN
Keyword(s):  
Information Technology ◽  
Face Recognition ◽  
Face Detection ◽  
New Information ◽  
Target Unit ◽  
Key Word ◽  
Working Principle ◽  
Car Security

ABSTRAK Kemajuan teknologi informasi telah banyak di manfaatkan dalam bidang keamanan terlebih disaat tindakan kriminal meningkat terutama dari pencurian. Mobil merupakan salah satu target unit yang harus di tingkatkan keamanannya, banyak para ahli menciptakan jenis keamanan pada mobil. Penulis mencoba untuk lebih mengembangkan dengan memanfaatkan Metoda Eigenface. Eigenface merupakan metoda yang memiliki prinsip kerja dengan menggunakan file XML dalam melakukan Recognition Face (pendeteksian wajah), hal ini hampir sama halnya dengan Face Detection. Pengaplikasian Face Recognition ini memiliki database berupa informasi wajah pemilik mobil yang sebelumnya telah disimpan kemudian dibandingkan oleh wajah yang telah ditangkap dan menghasilkan informasi baru berupa pengenalan wajah yang akan mengaktifkan alarm dan penguncian mobil otomatis. Kata kunci: Eigenface, File XML Pengenalan Wajah, Keamanan Mobil. ABSTRACT The development of information technology have been utilized on the security settings. Car is one of the target unit that should get it’s security improved, many experts create the kind of security on the car. The author tries to further develop car security by using eigenface method. Eigenface is a method which has the working principle using XML Files in performing face recognition (face detection), it is almost the same as face detection. The application of face recognition have database which contains information on car owners face, a process previously saved then compared by a face that has been captured and produce new information in the form of identification face that will activate the car alarm and automatic locking. Key Word: Eigenface, XML files, Face Recognition, Car Security,

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.

Editorial Introduction – Ethics of New Information Technology

2005 ◽  
Vol 7 (3) ◽  
pp. 109-109 ◽  
Author(s):  
Philip Brey ◽  
Luciano Floridi ◽  
Frances Grodzinsky
Keyword(s):  
Information Technology ◽  
Editorial Introduction ◽  
New Information

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 Embedded Smart Car Security System on Face Detection

2014 ◽  
pp. 176-180
Author(s):  
Vikram Kulkarni ◽  
Viswaprakash Babu
Keyword(s):  
Face Detection ◽  
Detection System ◽  
Security System ◽  
Experimental Results ◽  
Embedded Platform ◽  
Web Camera ◽  
The Face ◽  
Car Security ◽  
System Prototype

In this proposed embedded car security system, FDS(Face Detection System) is used to detect the face of the driver and compare it with the predefined face. For example, in the night when the car’s owner is sleeping and someone theft the car then FDS obtains images by one tiny web camera which can be hidden easily in somewhere in the car. FDS compares the obtained image with the predefined images if the image doesn’t match, then the information is sent to the owner through MMS. So now owner can obtain the image of the thief in his mobile as well as he can trace the location through GPS. The location of the car as well as its speed can be displayed to the owner through SMS. So by using this system, owner can identify the thief image as well as the location of the car This system prototype is built on the base of one embedded platform in which one SoC named “SEP4020”(works at 100MHz) controls all the processes .Experimental results illuminate the validity of this car security system.

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