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http://dx.doi.org/10.17661/jkiiect.2017.10.6.609

Face recognition using PCA and face direction information  

Kim, Seung-Jae (Department of Computer Engineering, Chosun University)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.10, no.6, 2017 , pp. 609-616 More about this Journal
Abstract
In this paper, we propose an algorithm to obtain more stable and high recognition rate by using left and right rotation information of input image in order to obtain a stable recognition rate in face recognition. The proposed algorithm uses the facial image as the input information in the web camera environment to reduce the size of the image and normalize the information about the brightness and color to obtain the improved recognition rate. We apply Principal Component Analysis (PCA) to the detected candidate regions to obtain feature vectors and classify faces. Also, In order to reduce the error rate range of the recognition rate, a set of data with the left and right $45^{\circ}$ rotation information is constructed considering the directionality of the input face image, and each feature vector is obtained with PCA. In order to obtain a stable recognition rate with the obtained feature vector, it is after scattered in the eigenspace and the final face is recognized by comparing euclidean distant distances to each feature. The PCA-based feature vector is low-dimensional data, but there is no problem in expressing the face, and the recognition speed can be fast because of the small amount of calculation. The method proposed in this paper can improve the safety and accuracy of recognition and recognition rate faster than other algorithms, and can be used for real-time recognition system.
Keywords
Face Detection; Face Recognition; Feature point; Orientation; PCA;
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