A Facial Recognition System is an excellent part of the computer vision industry, widely used in mobile phones, and security systems. The technique has become most popular, replacing password logins for users in their daily lives. However, many real-time factors influence facial images, such as noises, expressions, occlusions. It solves using an approach that proposes a new model for facial recognition with good robustness to different conditions. Some algorithms suggest noise reduction, and some techniques adaptively extend the training dataset to remove the bad data points. The results show that the performance of these algorithms is better than the traditional facial recognition methods. This blog will brief image processing techniques to improve Facial Recognition system.
Image Processing in Facial Recognition Systems
- Image Preprocessing Techniques
- Face Detection and Cropping
- Image De-noising and Filtering
- EigenFace based Approach
- Discrete Cosine Transform
- Combined Approach
6. Combined Approach
This technique forms using both spatial and frequency domains. While DCT extracts spatial domain data, spatial differential operators extract frequency-domain data.
An ambit reduction technique follows each Approach. First, PCA reduces the ambit of SDO features. In ratio, zonal coding reduces the dimensionality of DCT features.
Generally, Facial Recognition Solutions have been a source of excitement. More precisely than ever, the art to identify faces as images from algorithms and not human beings is an incredible technological achievement. However, the software is still very much in its infancy.
As systems continue to improve with new updates, we will see features like emotion detection and customization that make facial recognition programs more practical.
In addition, as technology evolves alongside current trends in ML and AI. It will be interesting to watch facial recognition systems grow over several years.