To ensure robust security, biometric liveness detection has become a critical component of many identity verification solutions. Liveness detection adds an essential layer to authentication strategies by enabling biometric systems to distinguish reliably between genuine users and spoofing attempts. This technology is now widely deployed in multiple forms, with the global face liveness detection market alone projected to surpass $250 million by 2027, according to a 2025 market report from Biometric Update and Goode Intelligence.
The risk of biometric spoofing
Biometric authentication solutions have evolved far beyond simply matching a biometric sample to an image template. As attack techniques become more sophisticated, capable of tricking sensors into recognizing fake inputs as legitimate authentication attempts, modern systems must also determine whether a biometric sample originates from a genuine human presence.
These attacks, known as presentation attacks, vary in complexity, expertise and cost. Small-scale attacks typically target individuals using easily produced replicas, while more advanced approaches focus on compromising systems at scale, including injection-style attacks designed to trigger false positives.
Countermeasures against such attacks include analyzing skin temperature, moisture, texture and electrical properties to distinguish between live and artificial inputs.
As digital identity becomes increasingly embedded in payments, public services and cross-border systems, the potential impact of biometric exploitation has grown significantly. For example the European Union’s Biometric Entry / Exit System (EES) began its implementation in October 2025, requiring non-EU citizens to have fingerprints scanned and a facial image taken at border checkpoints. In this context, any exploited weakness in biometric systems can have wide-ranging and systemic repercussions.
Starting point – standards and definitions
As with most technologies biometric systems are governed by standardized testing and certifications. In the case of anti-spoofing biometrics, sensors must comply with ISO / IEC 30107, which defines the international framework for Presentation Attack Detection (PAD). This framework provides a common way to describe, categorize, and assess spoofing attempts, enabling systems to be designed and evaluated against known attack vectors.
PAD testing assesses whether biometric sensors can reliably distinguish between bona-fide presentations and attacks using fake biometric samples.
However, adherence to standards alone does not guarantee resilience. ISO/IEC 30107 deliberately avoids prescribing specific detection techniques, recognizing that attack techniques will continue to evolve. This flexibility encourages innovation across hardware, software and machine-learning-based approaches, but also places the responsibility on vendors and deployers to implement robust, multi-layered defenses.
Where does liveness detection fit in the attack chain?
Collecting the data to attempt to create a spoof fingerprint can happen in a number of ways. These can range from anti-latent (removing the print from the sensor itself) through to non-cooperative spoofs like lifting the print from a glass.
Biometric spoofing attacks occur at the point of sensor input, where a fraudulent biometric trait is presented in an attempt to deceive the system. Liveness detection extends PAD by analyzing whether the fingerprint originates from a living human rather than a synthetic or manipulated replica.
A wide range of materials can be used to create these replicas, including:
- Silicone
- Play-Doh
- Gelatine
- White glue
- Latex
Without effective liveness detection, even high quality sensors can be bypassed if the spoof is sufficiently realistic, allowing the core matching algorithms to produce false positives.
Hardware + software = strong liveness detection
Leading biometric authentication technologies combine advanced sensors with sophisticated software and biometric algorithms to ensure strong liveness protection and attack resistance.
From a hardware perspective, some sensors measure physical characteristics that are inherently difficult to replicate. For example, technologies that assess thermal response and electrical conductivity at the point of contact add critical layers of verification that significantly raise the barrier for attackers.
By simultaneously scanning fingerprint ridges and analyzing thermal output the sensor can accurately detect live human fingerprints. This combination creates a strong defense against many common spoofing methods, as most synthetic materials fail to replicate the heat transfer properties of a real human finger.
This approach eliminates simple non-conductive or non-thermal spoofs such as rubber replicas. Latent prints are analyzed at the image level and utilize template matching, with the most recent verification template stored in non-volatile memory. Plausibility checks are performed prior to comparison to prevent replay or injection-style attacks.
When combined with anti-spoofing software and sophisticated biometric matching algorithms, these sensors can deliver a comprehensive PAD solution that remains effective even against more sophisticated spoofing techniques.
A layered approach to biometric verification
Biometric authentication is now embedded in everyday consumer services as well as critical national infrastructure. As attackers develop more advanced spoofing capabilities, robust liveness detection is essential. Organizations deploying biometric systems must treat liveness detection as a core design requirement rather than an optional enhancement.
By combining physical sensing techniques such as Active Thermal® with PAD algorithms and software-based liveness detection, biometric system providers can deliver resilient, future-proof authentication solutions that maintain security, trust and scalability over time.
Learn more about Active Thermal® technology in this area
Author: Kevin Hung, Director of Field Application Engineers at NEXT Biometrics