Enhanced AI-Driven Face Recognition: AO3: Enhancement of Performance Rate, Increase of Accuracy, and Enhancement of Robustness of Systems #3005
Conversation
In this update, the face recognition script developed using dlib has gone through some changes which are powered by strong artificial intelligence features to provide a better user interface and hoped to be improved for optimal performance. In the current script, there is a mechanism of AI enhanced image preprocessing where image quality is enhanced before face detection and it enhances the chances of our model giving better results under various conditions such as low resolution or poor lighting. New function called Dynamic face detection threshold has been employed here which makes the script more flexible and self-learning and can change its threshold according to the need of the image which provides a more refined detection in different cases. This process of feature extraction has also been improved with help of the AI algorithms which further enhances the 128D face descriptor vector and provides more accurate and qualitative recognition especially in cases when similar faces are involved. Also for alignment, the face alignment step has been enhanced to have a better face alignment algorithm using Artificial Intelligence that helps to boost the alignment for better face chips generation during recognition. There have been a number of enhancements which have been targeted, most notable among them being the script’s optimization, so as to increase the script’s speed of execution. This has entailed improved loops, efficient memory management, and implementation of artificial intelligence in processing the in-script, resulting in a faster and more efficient script. Some new features developed and incorporated are structured logging implemented for comprehensive and in-depth tracking of the script, intelligent error handling based on Artificial Intelligence for appreciable and efficient recognition and resolution of likely errors in order to improve the user experience. Last but not the least, in the script, we are now putting the parameters in the AI, known as jittering and padding, we provide the users with an opportunity to edit them as per their requirements.
AI-Enhanced Face Recognition Script: Enhancement of Accuracy and Performance and Reducing Hardware and Software Sensitivities
|
Thanks for the PR. End user applications should go in a separate repository though. You should make one in your account and post it there :) |
|
Warning: this issue has been inactive for 35 days and will be automatically closed on 2024-10-22 if there is no further activity. If you are waiting for a response but haven't received one it's possible your question is somehow inappropriate. E.g. it is off topic, you didn't follow the issue submission instructions, or your question is easily answerable by reading the FAQ, dlib's official compilation instructions, dlib's API documentation, or a Google search. |
|
Warning: this issue has been inactive for 42 days and will be automatically closed on 2024-10-22 if there is no further activity. If you are waiting for a response but haven't received one it's possible your question is somehow inappropriate. E.g. it is off topic, you didn't follow the issue submission instructions, or your question is easily answerable by reading the FAQ, dlib's official compilation instructions, dlib's API documentation, or a Google search. |
|
Notice: this issue has been closed because it has been inactive for 62 days. You may reopen this issue if it has been closed in error. |
However, in the recent update of the face recognition script using dlib major changes were made to increase its efficiency and to include AI functionalities. The script of the application was initially designed for face detection, facial landmarks and face descriptors generation. But it has issues concerning execution efficiency, errors dealing with, and enhanced flexibility.
In order to solve those, the given script was changed with several enhancements: Firstly, the dynamic error handling was added to enhance the information provided to the end-user and to avoid program termination in the cases when an incorrect input or a non-existing file is used. This makes it more reliable and easier to use to the various clientele. Also additional conditions were put into the loops and where possible restructuring of loops was done to improve efficiency of the code.
AI-related features were also incorporated to improve on the script’s functionality. The face detection and recognition were enhanced with optimization techniques based on artificial intelligence: face descriptor comparison adopted a variable threshold to respond to optimum detection for similar faces. There are new sliders in the script for the automatic alignment of images with faces using AI which helps to improve the orientation of face images before applying filters, which results in higher quality of the picture.
In addition, the script’s execution time was reduced to include batch processing with the aid of AI parallelism to enable the processing of multiple images at once without delay. This is especially important if one is dealing with big data as the results in this case are massive. The new features also include an AI module that offers an automatic facial landmark enhancement in order to improve the precision of the landmarks detected which is vital for an accurate face recognition.
As a whole, these changes have made the script more versatile and capable when compared to the previous implementation while providing corresponding practical features that can be used in face recognition tasks.