Fast Face Recognition on GPUs

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.

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Normalization of Facial Occlusion in Face Recognition

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k1754.0981119 ◽

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.

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A Method of 3D Face Recognition Based on Principal Component Analysis Algorithm

2005 IEEE International Symposium on Circuits and Systems ◽

10.1109/iscas.2005.1465311 ◽

2005 ◽

Cited By ~ 1

Author(s):

Xue Yuan ◽

Jianming Lu ◽

T. Yahagi

Keyword(s):

Principal Component Analysis ◽

Face Recognition ◽

Principal Component ◽

Component Analysis ◽

3D Face Recognition ◽

Analysis Algorithm ◽

3D Face ◽

Principal Component Analysis Algorithm

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Algorithmic Methodology Based Automobile Theft Detection and Prevention System

International Journal of New Practices in Management and Engineering ◽

10.17762/ijnpme.v2i03.18 ◽

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.

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