FACE DETECTION AND DEGENERATION SYSTEM BASED ON DEEP CONVOLUTIONAL NEURAL NETWORKS

Abstract

The present invention discloses a Face Detection and Degeneration System based on Deep Convolutional Neural Networks (CNNs), designed to efficiently detect and modify facial features in digital images or video streams. The system integrates high-performance hardware components, including a Graphics Processing Unit (GPU) and a Neural Processing Unit (NPU), optimized for real-time processing. The face detection model leverages advanced CNN architectures for accurate face localization, while a degeneration module applies transformations for privacy protection or synthetic face generation, utilizing Generative Adversarial Networks (GANs) or Autoencoders. The system is capable of processing high-resolution images, ensuring rapid and accurate face detection and modification. Its modular design allows for scalability, enabling deployment on various platforms, from embedded devices to cloud-based systems. This innovation addresses the need for efficient, real-time face detection and degeneration in applications such as privacy protection, biometrics, and synthetic media generation.

Specification

Description:[016] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit, and scope of the present disclosure as defined by the appended claims.
[017] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[018] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.
[019] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[020] The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.
[021] Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[022] In an embodiment of the invention and referring to Figures 1, the present invention relates to an advanced system and method for face detection and degeneration based on deep convolutional neural networks (CNNs). The invention employs a combination of sophisticated hardware and software components, designed to efficiently and accurately detect human faces in digital images or video streams, and further modify or degrade these faces for specific applications such as privacy protection and synthetic face generation. The system's design leverages state-of-the-art deep learning algorithms, efficient hardware components, and highly optimized software architectures to achieve real-time processing with high accuracy.
[023] The system employs a multi-component hardware setup consisting of high-performance processors, dedicated neural network processing units (NPUs), and memory subsystems. At the heart of the hardware is a Graphics Processing Unit (GPU) that accelerates the computations required for CNN-based face detection. The GPU is coupled with a Neural Processing Unit (NPU), a specialized hardware component designed specifically for running deep learning models efficiently. The NPU accelerates the matrix operations and convolutions necessary for processing the image data through the network layers, thus reducing latency and increasing throughput.
[024] The hardware also incorporates a high-speed memory subsystem, which includes both Random Access Memory (RAM) for short-term storage during processing and specialized storage devices such as Solid-State Drives (SSDs) for persistent storage of large datasets used in training and inferencing. The high-speed memory ensures quick data access and supports the processing of multiple frames per second in real-time applications such as live video feed analysis.
[025] Furthermore, the system uses a high-definition camera or image sensor module for input image acquisition. This module is designed to capture images at resolutions up to 4K, ensuring that the face detection system works with high-quality input, which is crucial for accurate face localization and feature extraction.
[026] On the software side, the system is built using an integrated suite of machine learning and image processing libraries. The primary software framework consists of TensorFlow or PyTorch, which are open-source deep learning frameworks capable of efficiently training and deploying deep CNN models. The system uses these frameworks to implement both the face detection and degeneration models, with custom layers added to enhance performance for facial feature localization and manipulation tasks.
[027] The face detection model is trained on a vast and diverse dataset, including variations of human faces under different lighting conditions, orientations, and occlusions. The model consists of several convolutional layers, followed by fully connected layers for classification. A region proposal network (RPN) is used to propose potential face regions, which are then refined using a bounding box regression technique. Once the face is detected, the image is passed through a secondary network for facial landmark detection, which localizes key facial features such as eyes, nose, and mouth.
[028] The face degeneration model uses an additional neural network that takes the detected facial landmarks and applies transformations to obfuscate or modify facial features for privacy protection or synthetic generation. This module uses adversarial networks (GANs) or autoencoders to perform controlled degeneration, either by altering facial structures or applying blurring techniques, depending on the application.
[029] The system operates in a multi-stage pipeline to first detect and then apply degeneration to the faces in the input image. Initially, the camera module captures the image and sends it to the GPU for processing. The first step in the pipeline is face detection, where the input image is passed through the CNN to identify potential face regions. These regions are then further processed to detect specific facial landmarks, such as the eyes, nose, and mouth.
[030] Once the face is detected and landmarks are identified, the degeneration module takes over. Depending on the specific requirements, such as privacy protection or synthetic face generation, the degeneration model applies a series of transformations to modify the facial features. For instance, in privacy mode, the system may blur the facial features, while in synthetic mode, it may generate a new set of features or distort the existing ones to simulate aging or generate entirely new faces.
[031] The system is designed to process multiple frames per second, ensuring that real-time face detection and degeneration can occur simultaneously. The integration between the detection and degeneration modules allows for smooth transitions and highly accurate modifications to the detected faces.
[032] The interconnection between hardware and software components is key to the system's efficiency. The GPU, along with the NPU, handles all the heavy computations involved in running the deep CNNs. These units communicate with the memory subsystem to fetch data and store intermediate results. The software layer running on top of the hardware coordinates the operations, managing data flow from the camera module to the CNN, and subsequently to the degeneration model.
[033] The data flow starts with the camera capturing an image, which is processed by the software framework to detect faces using CNN-based algorithms. Once a face is detected, the image data is sent to the degeneration module for further processing. Throughout the process, the hardware accelerators ensure that the computations are performed with minimal delay, allowing for real-time face detection and degeneration.
[034] Real-time processing is a critical feature of the present invention. The system employs several optimization techniques to ensure high throughput and low latency. One such technique is the use of model pruning, where redundant parameters are removed from the trained CNN to reduce the model's size and improve inference speed. Additionally, data parallelism is employed, where the image data is processed in parallel across multiple processing units, further speeding up the face detection and degeneration tasks.
[035] To handle large-scale datasets, the system employs efficient data loading techniques, where images are pre-processed and loaded into memory in batches. This reduces the time taken to load data into the GPU and ensures that the system can process real-time video feeds without significant delays.
[036] The performance of the system can be evaluated using several key metrics, such as face detection accuracy, degeneration quality, and processing speed. To validate the efficacy of the system, a set of tests is performed on a benchmark dataset consisting of images with various levels of occlusion, lighting variations, and face orientations. The system's accuracy in detecting faces is compared with existing face detection algorithms, such as Haar cascades and HOG-based methods.
[037] Additionally, the degeneration quality is assessed by comparing the modified faces against original, unaltered images using metrics like Mean Squared Error (MSE) and Structural Similarity Index (SSIM). Real-time performance is measured in terms of frames per second (FPS), with the goal of achieving at least 30 FPS for real-time video processing.
Table 1: Comparison of System Performance with Existing Methods

[038] From the table, it is evident that the present invention significantly outperforms traditional methods in both accuracy and real-time processing speed, while also introducing a new capability in face degeneration.
[039] One of the core features of the system is its ability to perform face degeneration for privacy protection. In environments such as public surveillance or social media platforms, the system can apply selective degeneration to faces detected in real-time. For example, the system can obscure faces in public spaces to protect the privacy of individuals, while maintaining the integrity of the surrounding environment. This selective privacy feature is achieved by controlling the extent of degeneration applied to the detected faces, ensuring that the underlying context is preserved.
[040] The system is designed with scalability in mind. By decoupling the detection and degeneration modules, the system allows for the easy integration of additional functionalities, such as emotion recognition or age progression. Furthermore, the modularity of the hardware and software components allows the system to be scaled from small, embedded devices to large-scale cloud-based platforms. This ensures that the invention can be adapted for a wide range of applications, from mobile devices to large-scale surveillance systems.
[041] The present invention has wide-ranging applications across various industries. In security and surveillance, the system can be used for real-time face detection and privacy protection, ensuring that sensitive information is not exposed. In the field of biometrics, the system can enhance facial recognition accuracy by providing controlled degeneration to improve recognition performance. Additionally, the system can be used in entertainment and virtual reality for generating synthetic faces or manipulating facial features to create lifelike avatars.
[042] In conclusion, the present invention provides a highly efficient and accurate face detection and degeneration system, leveraging deep convolutional neural networks along with specialized hardware and optimized software components. The system's ability to perform real-time processing while ensuring high accuracy and degeneration quality sets it apart from existing technologies, offering significant advancements in face detection, privacy protection, and synthetic face generation. , Claims:1. A Face Detection and Degeneration System based on Deep Convolutional Neural Networks (CNNs) is provided, comprising:
a) a camera module configured to capture images of a subject;
b) a multi-component hardware setup including a Graphics Processing Unit (GPU) and a Neural Processing Unit (NPU), wherein the GPU accelerates computations for CNN-based face detection and the NPU handles matrix operations for deep learning model execution;
c) a deep convolutional neural network configured to detect faces within the captured images;
d) a degeneration module including an additional neural network to apply transformations on detected faces, wherein the transformations are selected from the group consisting of obfuscation for privacy protection and synthetic face generation;
e) a memory subsystem coupled with the hardware setup, storing image data, neural network models, and intermediate results, to ensure high-speed processing and low-latency for real-time applications.
2. The system as claimed in claim 1, wherein the face detection model utilizes a Region Proposal Network (RPN) to propose potential face regions and a bounding box regression technique to refine these proposed regions.
3. The system as claimed in claim 1, wherein the degeneration module utilizes Generative Adversarial Networks (GANs) or Autoencoders to alter facial features in real-time, either by blurring facial structures for privacy or by simulating aging or generating entirely new faces for synthetic applications.
4. The system as claimed in claim 1, wherein the hardware setup further includes high-speed memory components, including Random Access Memory (RAM) for short-term storage during processing and Solid-State Drives (SSDs) for persistent storage of training and inferencing data.
5. The system as claimed in claim 1, wherein the camera module is capable of capturing images at a resolution of at least 4K to ensure high-quality input for accurate face detection and feature extraction.
6. The system as claimed in claim 1, wherein the real-time processing is optimized through model pruning, reducing the size of the trained CNN model to improve inference speed and minimize computational load.
7. The system as claimed in claim 1, wherein data parallelism is employed, allowing for simultaneous processing of image data across multiple processing units to improve the speed of both face detection and degeneration tasks.
8. The system as claimed in claim 1, wherein the degeneration module selectively applies face degeneration depending on user settings, with options to either blur, alter, or fully synthesize facial features based on the privacy or synthetic face generation mode selected.
9. The system as claimed in claim 1, wherein the degeneration module is capable of handling multiple face degeneration transformations in real-time when multiple faces are detected within the same image or video feed.
10. The system as claimed in claim 1, wherein the system is scalable, allowing for deployment on devices ranging from embedded systems with limited hardware resources to large-scale, cloud-based platforms, with modular integration for additional functionalities such as emotion recognition or age progression.

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