FACE RECOGNITION USING LOCAL BINARY PATTERN ALGORITHM

Abstract

Face recognition is a prominent field in computer vision that aims to automatically identify individuals based on their facial characteristics. In this study, we propose a face recognition system utilizing the Local Binary Pattern (LBP) algorithm. LBP is a texture descriptor that characterizes the local spatial patterns within an image, which can effectively capture the discriminative features of facial textures. The proposed face recognition system consists of two main stages: training and testing. In the training stage, a database of face images is utilized to extract LBP features from facial regions of interest. These features are then used to train a classification model, such as a support vector machine (SVM) or a neural network, to learn the discriminative patterns associated with each individual in the database. During the testing stage, the LBP algorithm is applied to extract features from an input face image. These features are compared with the learned patterns in the classification model to determine the identity of the individual. The proposed system provides robustness against variations in illumination, facial expressions, and pose, making it suitable for real-world face recognition applications. To evaluate the performance of the proposed system, extensive experiments were conducted on benchmark face databases, including LFW, CASIA-Web Face, and FRGC. The results demonstrate that the LBP-based face recognition system achieves competitive accuracy rates compared to state-of-the-art methods, while maintaining computational efficiency. This study presents a face recognition system based on the Local Binary Pattern algorithm, which offers a robust and efficient solution for identifying individuals based on their facial characteristics. The proposed system holds great potential for various applications, including security systems, access control, and surveillance technologies.