Smile for AI Blog Series: What Really Happens When You Take That Selfie?
15 January 2024

Smile for AI Blog Series: What Really Happens When You Take That Selfie?

15 January 2024

To kick off the new year with excitement, we're thrilled to introduce our new blog series, "Smile for AI". Throughout Q1 of 2024, we'll delve into the factors fueling its popularity, draw insightful comparisons with other biometric methods, and highlight the most impactful use cases. Stay tuned for a captivating journey into the fascinating realm of AI and facial biometrics!


Even within the rapidly evolving world of cybersecurity, facial biometrics has truly become a cutting-edge technology rapidly transforming how we approach identification and security. According to leading digital identity strategy firm Liminal, the facial biometrics market is not just growing—it's booming, with a projected value of $42.1 billion by 2028.

Driven by widespread adoption in both the public and private sectors, facial biometrics is at the forefront of technological innovation. However, this journey isn't without its challenges. The evolving global regulatory landscape and the rise of AI-generated deepfakes and spoofing technologies present hurdles that the industry must navigate.

Nonetheless, the demand for seamless and secure identification processes, especially among younger consumers, continues to propel this market forward. In this blog, we will cover the basics of the facial biometric process - what happens, and when.

Step 1: Image Capture

The journey begins with the capture of an individual's facial images using devices like cameras, webcams, or smartphones. This step is crucial as it lays the foundation for further processing.

Step 2: Preprocessing

Once captured, these images undergo preprocessing. This involves enhancing image quality by removing noise, cropping, resizing, and filtering, ensuring the data is primed for accurate analysis.

Step 3: Image Normalization

Normalization follows, where images are adjusted to a standard scale. This step prevents certain features from dominating others, maintaining a balanced analysis.

Step 4: Face Detection and Alignment

Now comes the detection and alignment phase. Algorithms work tirelessly to identify the position and orientation of the face, aligning it to a standardized format. This consistency is vital for accurate feature extraction.

Step 5: Feature Extraction

Here, the system identifies distinct facial landmarks. The distance between the eyes, the shape of the nose, and the curvature of the lips are meticulously analyzed using both 2D and 3D modeling.

Step 6: Feature Encoding

The extracted features are then transformed into mathematical templates. This involves several normalization processes like adjusting pixel intensities (gray level normalization), correcting for light impact (illumination normalization), and adjusting for position and scale (geometry normalization).

Step 7: Matching and Decision Making

This final phase is crucial. The biometric system matches the created template against a database of pre-registered templates or a previously stored personal reference. This step is pivotal in determining identity or verifying a user's authenticity. The system uses sophisticated algorithms to calculate a similarity score. If this score surpasses a predetermined threshold, a match is declared; otherwise, it results in an inconclusive outcome. It's a delicate balance of precision and accuracy, ensuring reliable results while maintaining privacy and security.

The facial biometrics field is rapidly evolving, facing both technological advancements and challenges. Deepfake technology, which creates highly realistic and deceptive images, poses a significant threat to the integrity of facial biometrics systems. However, the industry is responding with advanced countermeasures, constantly improving the technology to stay ahead of such threats.

In the final stage, these templates are compared against a database of pre-registered templates or a stored personal reference sample. The decision hinges on whether the similarity score surpasses a predetermined threshold. If it does, a match is declared; if not, the result is deemed inconclusive.

The Future of Facial Biometrics

As facial biometrics technology advances, so does its application spectrum. It's no longer confined to traditional security realms; it's making waves in areas like mobile payment authorization and digital onboarding, providing a frictionless user experience. This expansion, however, brings to light the critical issue of regulatory compliance. With legislation like BIPA influencing the industry, facial biometric vendors are compelled to adapt and innovate, ensuring their solutions are not only technologically advanced but also legally compliant.

Despite these challenges, the demand for facial biometrics remains robust, especially in public-facing industries that prioritize secure and seamless identity verification. The technology's ability to provide enhanced security while offering a user-friendly experience is a key factor driving its adoption. It is a testament to the technology's potential and its critical role in shaping the future of digital identity verification.

In our next blog, we will discuss "Why Now?" — an insightful look at the current landscape of facial biometrics. We'll delve into the reasons behind its rising prominence, fueled by headline-grabbing BIPA litigation, the growing challenge of deepfake spoofing, and the adoption of facial biometrics in public-facing industries for cross-border solutions.

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