The widespread usage of mobile devices and social media has led to a growing interest in face recognition technology. This study introduces a novel deep ensemble method designed to enhance facial recognition accuracy on a mobile selfie dataset by integrating three pre-trained models, viz. Inception-v3, ResNet-50, and EfficientNet B7 for automatic feature extraction and representation. The approach utilizes feature-level fusion through concatenation, followed by dimensionality reduction via principal component analysis (PCA). Feature optimization is carried out using the Firefly algorithm, and classification is achieved through a soft voting ensemble of classifiers, including Support Vector Machine (SVM), Random Forest, and a Deep Neural Network (DNN). When evaluated on the LFW, UTK face, and Wild Selfie datasets, the proposed method achieved recognition accuracies of 99.76%, 98.92%, and 98.73%, respectively, demonstrating competitive and significantly improved performance over existing models. The results indicate that the system performs effectively in real-world conditions, especially in environments with varying conditions.