scholarly journals Normalization of Facial Occlusion in Face Recognition

2019 ◽  
Vol 8 (11) ◽  
pp. 2523-2527
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Face Detection ◽  
Error Rate ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Principal Component ◽  
Normalization Method ◽  
Analysis Algorithm ◽  
Principal Component Analysis Algorithm

Face recognition accuracy is determined by face detection results. Detected faces will be in view of clear and occlusion faces. If detected face has occlusion than recognition accuracy is reduced. This research is directed to increase recognition rate when detected occlusion face. In this paper is proposed normalization occlusion faces by Principal component analysis algorithm. After applying normalization method in occlusion faces false reject error rate is decreased.

scholarly journals Algorithmic Methodology Based Automobile Theft Detection and Prevention System

2013 ◽  
Vol 2 (03) ◽  
pp. 06-11
Author(s):  
Ms. Monika Soni
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Facial Expressions ◽  
Face Detection ◽  
Principal Component ◽  
Detection Algorithm ◽  
Algorithmic Approach ◽  
Analysis Algorithm ◽  
Prevention System ◽  
Principal Component Analysis Algorithm

To prevent and identifying the theft problem, smart car is an ultimate solution. When a person enters into car, automatically takes the photos of driver .Using Principal Component analysis algorithm, checks the photos of driver already stored in the database and decide the person is authorized or unauthorized.   If the person is authorized, the person can access the vehicle. When the person is unauthorized. Using GSM and MMS modem, send messages to the user’s mobile number and then the car speed gets slow down. The ignition unit of the car can stops and the door cannot open. Using GPS, the location of the car and thief can easily identified using algorithmic approach. Any facial expressions and background conditions can changes in images, detection cannot takes place. To avoid this problem, face recognition and face detection algorithm can be used.

Fast Face Recognition on GPUs

2014 ◽  
Vol 905 ◽  
pp. 543-547
Author(s):  
Yi Lei ◽  
Xiao Ya Fan ◽  
Meng Zhang
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Face Recognition ◽  
Processing Speed ◽  
Principal Component ◽  
Great Accuracy ◽  
Analysis Algorithm ◽  
Training Images ◽  
Principal Component Analysis Algorithm

Face recognition is popular in the field of pattern recognition and image processing. However, traditional recognition technologies spend too long there are a lot of images to be recognized or trained for great accuracy in the recognition. Parallel computing is an effective way to improve the processing speed. With the improvement of GPU performance, its widely applied in computing-concentrated data operations. This paper presents a study of performance speedup achieved by applying GPU for face recognition based on PCA (Principal Component Analysis) algorithm. We successfully accelerated the testing phase by 6868-folds compared to a sequential C implementation when it has 100 test images and 2400 training images.

A License Plate Recognition Method Based on Wavelet Transform and Symmetrical Principal Component Analysis Algorithm

2013 ◽  
Vol 427-429 ◽  
pp. 1743-1746
Author(s):  
Xue Feng Deng
Keyword(s):  
Principal Component Analysis ◽  
Wavelet Transform ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
Analysis Algorithm ◽  
Principal Component Analysis Algorithm

In the past, the license plate recognition algorithm has some shortcomings, such as low recognition rate, slow speed of recognition, inaccurate license plate positioning. This paper proposes a new license plate location algorithm based on wavelet transform and the principal component analysis algorithm is used to feature extraction.The experimental results show that this method can reduce the amount of computation and improve the system recognition rate.

Low-Resolution Face Recognition of Multi-Scale Blocking CS-LBP and Weighted PCA

2016 ◽  
Vol 30 (08) ◽  
pp. 1656005 ◽  
Author(s):  
Jiadi Li ◽  
Zhenxue Chen ◽  
Chengyun Liu
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Principal Component ◽  
Low Resolution ◽  
Multi Scale ◽  
Face Features ◽  
Weighted Principal Component ◽  
Pca Algorithm

A novel method is proposed in this paper to improve the recognition accuracy of Local Binary Pattern (LBP) on low-resolution face recognition. More precise descriptors and effectively face features can be extracted by combining multi-scale blocking center symmetric local binary pattern (CS-LBP) based on Gaussian pyramids and weighted principal component analysis (PCA) on low-resolution condition. Firstly, the features statistical histograms of face images are calculated by multi-scale blocking CS-LBP operator. Secondly, the stronger classification and lower dimension features can be got by applying weighted PCA algorithm. Finally, the different classifiers are used to select the optimal classification categories of low-resolution face set and calculate the recognition rate. The results in the ORL human face databases show that recognition rate can get 89.38% when the resolution of face image drops to 12[Formula: see text]10 pixel and basically satisfy the practical requirements of recognition. The further comparison of other descriptors and experiments from videos proved that the novel algorithm can improve recognition accuracy.

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