Limitations of Facial Recognition Technology in Today’s World

By virtue of the inherent limitations of facial recognition technology, companies need to be aware of its applications

Facial recognition technology is creating a lot of buzzes. However, there is also considerable debate about privacy, dependability, potential bias, and a lack of control. Because of this, companies need to be aware of any facial recognition technology limitations.

Here are the four limitations of facial recognition technology

1. Poor Image Quality

The effectiveness of facial-recognition algorithms is influenced by the image quality. When compared to a digital camera, the quality of the scanned video is relatively poor. Even high-definition video is typically 720p, but it can be as high as 1080p. These numbers correspond to around 2MP and 0.9MP, although a low-cost digital camera may capture 15MP. The difference is clear to see.

2. Small Image Sizes

How successfully a face will be identified depends on its relative size to the total image size when a face-detection algorithm discovers a face in an image or in a still from a video recording. The recognized face is just 100 to 200 pixels wide due to the already modest image size and the target's distance from the camera. Furthermore, it takes a lot of processing power to scan an image for different face sizes. To reduce false positives during detection and hasten image processing, the majority of algorithms allow for the choice of a face-size range.

3. Different Face Angles

The relative angle of the target's face has a significant impact on the recognition score. Usually, several angles are employed when enrolling a face in the facial recognition software. The algorithm's capacity to create a face template is impacted by any view other than a frontal view. The rating of any resulting matches increases with the directness and the image's resolution.

4. Data Processing and Storage

The high-definition video takes up a lot of disc space despite having a resolution that is much lower than that of digital camera images. Processing every frame of video would be a huge job, thus typically only a small portion (10% to 25%) is subjected to a recognition system. Agencies may employ computer clusters to reduce overall processing time. However, adding computers necessitates a significant amount of data transfer through a network, which may be constrained by input-output limitations, further slowing down processing.

Unfortunately, when it comes to facial recognition, people outperform technology by a wide margin. However, when watching a source video, humans can only focus on a small number of people at once. Many people can be compared to a database of thousands of people by a computer.