PCA effect on the 3D face recognition system speed

Abstract

In this paper, a system of three-dimensional (3D) face recognition is not done through 3D face reconstruction method but directly uses the data retrieved from Kinect Xbox camera system. From a previous study, there exists a possibility to increase the speed and accuracy of the system. In order to accelerate the recognition speed, a single step in the said study is eliminated, which is the reconstruction of 3D face data. The algorithms used in this research are Backpropagation Neural Network and PCA. Testing is done in two ways. The first test uses a combination of Backpropagation and PCA, while the second test uses Backpropagation only. PCA has the function of simplifying the amount of data, thereby reducing the amount of computing that can increase speed system. The results show that the system that uses a combination of Backpropagation and PCA achieved an increase in speed up to 34.2 times, but a reduced accuracy of 8.5%.

Recognition Algorithm of Popular Elements of Ethnic Minority Traditional Clothing Based on PCA

“…Its principle is to extract the main components of ethnic minority traditional clothing by using K-L transform, construct the feature popular element space, project the test image to this space, obtain a set of projection coefficients, and identify it by comparing with each popular element. PCA method has achieved a good recognition effect, but the amount of calculation is large [7]. Wavelet transform denoises and extracts features from the image by constructing a wavelet basis, which not only effectively avoids the interference of noise and redundant data [8] but also accurately locates the boundary points, which is helpful to improve the eigenvalues of popular elements in ethnic minority clothing.…”