FACES OF THE FUTURE: EXPLORING ADVANCED AUTOMATED FACE RECOGNITION IN THE DIGITAL ERA

An all-encompassing automated system for video surveillance, dedicated to recognizing faces, consists of various elements: face detection, face alignment, face recognition, and alert generation. In today's world, face recognition has become a powerful technology utilized in numerous applications, particularly in criminal identification. The ongoing manual examination of surveillance videos is an arduous process that demands significant visual focus but lacks mental engagement, making it prone to mistakes. Therefore, this book presents an automated facial recognition system as a solution to tackle these obstacles.

The work consisted of three distinct phases. Initially, the author conducted an evaluation of multiple existing face detection algorithms. After careful analysis, it is determined that the Single-Shot Multibox Detector (SSD) is the most optimal method due to its superior speed and accuracy compared to other alternatives. In the following phase, a new model for face recognition based on ensemble learning is introduced. Recognizing faces has proven challenging due to factors such as pose variations, changes in lighting, aging effects, partial occlusion, and low resolution. Contemporary approaches to face recognition have limitations when dealing with these unconstrained conditions. Therefore, improving face recognition requires incorporating diverse deep learning architectures. Despite advancements in traditional deep learning techniques for face recognition systems, there is still a need for a robust and efficient solution. To address this gap, the work given in this book used Hybrid Ensemble Convolutional Neural Network (HE-CNN) model. This model is established through ensemble transfer learning from modified pre-trained models and contributes to achieving higher accuracy in face recognition tasks.

The model undergoes a two-phase training approach, incorporating a differential learning rate based on a one-cycle policy. This method greatly improves the model's ability to recognize faces. It should be noted that these enhancements result in State-of-the-Art performance. To achieve this, the concatenation of Global Max Pooling (GMP) and Global Average Pooling (GAP), Batch Normalization (BN), a Fully Connected (FC) layer, and dropout are integrated into the classification layers of pre-trained models. The incorporation of these suggested modifications and refining of the training process, the author observed outstanding results with a significant increase in recognition accuracy. The HE-CNN model has been evaluated using a self-curated criminal dataset to demonstrate its real-time applicability in practical scenarios. Through careful parameter selection and customization of layers, the designed model achieved remarkable accuracy of 95% on the self-curated dataset. Lastly, in the presented work given in this book, an automated alert system has been created that identifies crime-prone areas and helps prevent criminal activities. This is done through the analysis of data obtained from the identification of criminals. The system proactively alerts law enforcement personnel about high-risk areas so they can be prepared and vigilant before any crimes occur. Alerts are sent promptly when individuals with criminal records are detected in specified regions.

In a gist, the book, “Faces of the Future: Exploring Advanced Automated Face Recognition in the Digital Era”, provided an efficient face recognition system based on the hybrid model. The hybrid model leverages the benefits of deep ensemble transfer learning techniques to construct a fast and highly accurate model.

The structure of the book is organized into six chapters, including the introduction chapter as Chapter 1. In Chapter 2, a comprehensive exploration of existing techniques for Face Recognition is undertaken, starting with traditional algorithms and progressing to advanced deep learning-based approaches, transfer learning-based methods, and ensemble learning-based techniques. The chapter also discusses the standard datasets that can be used to evaluate FR algorithms.

In Chapter 3, techniques to optimize deep learning models are explored. Similarly, Chapter 4 gives the idea about the available deep learning frameworks to implement face recognition algorithms.

Chapter 5 addresses the challenging areas in Face Recognition. In 6, dataset pre-processing techniques are introduced. An algorithm for data oversampling is introduced in this chapter as part of an effort to ensure that a balanced dataset is used in evaluating the face recognition algorithm. In Chapter 7, metrics such as accuracy, precision, recall, F1-score, ROC curve are discussed to evaluate face recognition algorithms.

Chapter 8 introduces an automated method for FR system. Deep ensemble transfer learning is used in the proposed system to strike a balance between accuracy and computational resources. In the proposed and implemented system, face detection is handled by SSD, while Face Recognition is handled by a hybrid model. The suggested FR system also includes alert generation to reduce human intervention in recognizing individuals.

Finally, in Chapter 9, the book is summarized, conclusions are drawn, and future research directions are explored.