Leveraging Lightweight Hybrid Ensemble Distillation (HED) for Suspect Identification With Face Recognition

Face recognition biometric systems focus on identifying individuals by extracting their facial characteristics. However, these systems often fail or are misclassified because of external factors, obstructions, and varying environmental conditions. Traditional models cannot effectively handle these variations, leading to inaccuracies. Moreover, the complexity and computational demands of advanced models can hinder their real-time application. In this study, the Hybrid Ensemble Distillation (HED) model addresses these issues by leveraging both knowledge distillation and an ensemble of pre-trained models (VGG16, ResNet50, and DenseNet121) to enhance the precision and proficiency of categorization. The model combines the strengths of these architectures while utilizing data augmentation techniques such as GANs to enhance the training dataset. The proposed model demonstrated high efficiency and accuracy, with the teacher model achieving 98.42% accuracy and the student model reaching 96.78% validation accuracy, thereby highlighting the efficacy of knowledge distillation. It also showed progressive improvements in the validation accuracy and loss reduction over 350 epochs, emphasizing the robustness of the training process. This lightweight method helps identify suspects or individuals because the model was trained using 360-degree images in the dataset, ensuring comprehensive feature extraction from multiple angles. The reduced computational requirements and high accuracy make this approach suitable for real-time applications, thereby enhancing its practicality for various human identification tasks.