Face recognition processes often take into account the orientation and illumination of the face to achieve a satisfactory recognition rate. Additionally, extracting features aimed at capturing expressions is crucial for expression recognition. By understanding emotions through facial and expression recognition, it becomes possible to monitor and manage office environments more effectively, aiding in people management. This research aims to identify specific features, including facial orientation, to enhance the recognition rate. The cutting-edge solution incorporates facial landmarks, affine transformation, and advanced feature extraction techniques utilizing the Attention module and Local Binary Pattern (LBP), addressing issues related to face orientation, illumination, and targeted features. This paper introduces an improved face recognition system that employs a preprocessing layer incorporating facial landmarks and affine transformation, a modified attention module, and circular derivative LBP within the present state-of-the-art framework. The proposed model also includes new data augmentation methods, such as shear and zoom, augmenting the base method. The system’s evaluation spans four datasets: MMA, JAFFE, FER2013, and CKplus. The proposed system achieves an average accuracy of 95.63%, surpassing the state-of-the-art accuracy of 93.72%. Furthermore, it reduces processing time from 32.54 ms to 26.45 ms. Additionally, the proposed system achieves higher average F1 scores and AUC values of 87.66 and 88.98, respectively, compared to the state-of-the-art solution. These results demonstrate that the proposed system enhances the face recognition rate by employing a modified feature extraction process, effectively addressing intra- and inter-class separation challenges.