Improving Accuracy in Face Recognition Proposal to Create a Hybrid Photo Indexing Algorithm, Consisting of Principal Component Analysis and a Triangular Algorithm (PCAaTA)

Abstract

Accurate face recognition is today vital, principally for reasons of security. Current methods employ algorithms that index (classify) important features of human faces. There are many current studies in this¯eld but most current solutions have signi¯cant limitations. Principal Component Analysis (PCA) is one of the best facial recognition algorithms. However, there are some noises that could a®ect the accuracy of this algorithm. The PCA works well with the support of preprocessing steps such as illumination reduction, background removal and color conversion. Some current solutions have shown results when using a combination of PCA and preprocessing steps. This paper proposes a hybrid solution in face recognition using PCA as the main algorithm with the support of a triangular algorithm in face normalization in order to enhance indexing accuracy. To evaluate the accuracy of the proposed hybrid indexing algorithm, the PCAaTA is tested and the results are compared with current solutions.

Human Ear Image Recognition Method Using PCA and Fisherface Complementary Double Feature Extraction

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Analysis of Accuracy Rate of Distributed Virtual Reality Face Recognition Based on Personal Intelligent Terminal

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