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 field but most current solutions have significant limitations. Principal Component Analysis (PCA) is one of the best facial recognition algorithms. However, there are some noises that could affect 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.
|Number of pages||21|
|Journal||International Journal of Pattern Recognition and Artificial Intelligence|
|Early online date||16 Aug 2016|
|Publication status||Published - 01 Jan 2017|