E-CNN-FFE: An Enhanced Convolutional Neural Network for Facial Feature Extraction and Its Comparative Analysis with FaceNet, DeepID, and LBPH Methods

Facial feature extraction plays a pivotal role in modern-day computer vision tasks, and the effectiveness of these methods is imperative for applications ranging from facial recognition to emotion detection. In this paper, we introduce E-CNN-FFE, an Enhanced Convolutional Neural Network designed specifically for Facial Feature Extraction. Built on the foundation of existing convolutional neural network (CNN) architectures, E-CNN-FFE incorporates novel modifications to optimize the extraction of intricate facial features, aiming for enhanced performance in both accuracy and computational efficiency. We embark on a comprehensive comparative analysis, positioning E-CNN-FFE against established algorithms: FaceNet, DeepID, and LBPH. Evaluations are carried out in terms of feature extraction capability, accuracy, computational speed, and robustness against varying facial conditions and distortions. Preliminary results suggest that E-CNN-FFE showcases significant improvements in specific domains over its counterparts, elucidating its potential as a robust and reliable tool for facial feature extraction tasks. The implications of E-CNN-FFE span across various facial recognition applications, potentially setting a new benchmark in the field. This study not only offers insights into the algorithmic enhancements of E-CNN-FFE but also serves as a comprehensive guide for researchers and practitioners aiming to harness the power of neural networks for facial analysis tasks.