Abstract
Facial recognition has attracted the attention of researchers and has been one of the most prominent topics in the fields of image processing and pattern recognition since 1990. This resulted in a very large number of recognition methods and techniques with the aim of increasing the accuracy and robustness of existing systems. Many techniques have been developed to address the challenges and reliable recognition systems have been reached but require considerable processing time, suffer from high memory consumption and are relatively complex. The focus of this paper is on extracting subset of descriptors (less correlated and less calculations) from the co-occurrence matrix with the goal of enhancing the performance of Haralick’s descriptors. Improvements are achieved by adding the image pre-processing and selecting the proper method according to the database problem and by extracting features from image local regions.
Highlights
Traditional authentication systems are classified into knowledge-based systems and token-based systems
The main problems of these systems are the possibility of forgetting passwords, forgery, loss of identification cards, card damage
Due to the existence of varying lighting and age, the local features were introduced in face recognition systems. [3] presented a new and effective representation of face image based on the texture features Local Binary Pattern( LBP), which extracted locally from the image regions because they are more resistant to challenges of changing such as pose and lighting
Summary
Traditional authentication systems are classified into knowledge-based systems and token-based systems. One important problem in face recognition is finding efficient descriptors for extracting the useful and discriminant features from facial images. [3] presented a new and effective representation of face image based on the texture features Local Binary Pattern( LBP), which extracted locally from the image regions because they are more resistant to challenges of changing such as pose and lighting. Finding good descriptors for local facial regions is an open issue These descriptors should be easy to calculate and be able to distinguish between different individuals and be robust against changes that occur to the same person. [9] proposed a new, simple and straightforward method to face recognition based on features extracted from GLCM and focused on the importance of quantization process. No 1. 2020) (pp.10-15) accuracy of their distinction through that local approach and adding pre-processing step
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