Is Facial Recognition Accurate?
The accuracy of facial recognition can be decreased in real-life situations as these systems take a hit under those settings. Some of the key drivers for bias have been summarized here:
- Controlled environment: Algorithms are able to successfully identify and match faces with reference images taken under controlled lighting conditions with quality cameras, giving accuracies of almost 99.97%.
- Aging: Accuracy suffers from the natural alteration of features taking place over the years, especially with the photos taken with the years of gap.
- Demographic distortions: The system sometimes tends to perform better for lighter skin and male genders and the error rates are higher for women and people of color.
- External factors: Low-resolution cameras, digital noise, and changing expressions adversely affect the performance.
Is Facial Recognition Safe?
Being based on unique biometric patterns, facial recognition systems are perhaps one of the safest modes of identification between existing modes in biometric technology. Liveness detection, in turn, guarantees that the system interacts only with live users, establishing a countermeasure against spoofing attacks using photos or videos.
Yet there are concerns regarding privacy and misuse, such as mass surveillance which underlines the need for strict regulatory mechanisms, administered within an ethical ambit.
Data Collection for Facial Recognition Model
For the facial recognition model to perform to its maximum efficiency, you must train it on various heterogeneous datasets.
Since facial biometrics differs from person to person, the facial recognition software should be adept at reading, identifying, and recognizing every face. Moreover, when the person shows emotions, their facial contours change. The recognition software should be designed so that it can accommodate these changes.
One solution is receiving photos of several people from various parts of the world and creating a heterogeneous database of known faces. You should ideally take photos from multiple angles, perspectives and with a variety of facial expressions.
When these photos are uploaded to a centralized platform, clearly mentioning the expression and perspective, it creates an effective database. The quality control team can then sift through these photos for quick quality checks. This method of collecting pictures of different people can result in a database of high-quality, highly-efficient images.
Wouldn’t you agree that facial recognition software will not work optimally without a reliable facial data collection system?
Facial data collection is the foundation for any facial recognition software’s performance. It provides valuable information such as the length of the nose, the width of the forehead, the shape of the mouth, ears, face, and much more. Using AI training data, automated facial recognition systems can accurately identify a face amidst a large crowd in a dynamically changing environment based on their facial features.
If you have a project that demands a highly reliable dataset that can help you develop sophisticated facial recognition software, Shaip is the right choice. We have an extensive collection of facial datasets optimized for training specialized solutions for various projects.
To know more about our collection methods, quality control systems, and customization techniques, get in touch with us today.