With the rise of deepfakes and sophisticated identity spoofing, it’s becoming increasingly difficult for companies to ensure that the person on the other end is who they claim to be. Identity verification has always been of paramount importance in sensitive industries like finance and security, but as fraud becomes more convoluted, it’s imperative that organizations take action quickly. That’s where biometric liveness detection comes in.
What is Biometric Liveness Detection?
Biometric liveness detection ensures that the biometric features being presented to a system—whether it’s a fingerprint, facial scan, voiceprint, or even an iris pattern—are genuinely from the living individual and not from a fraudulent representation.
For instance, in facial recognition systems, liveness detection could help differentiate between a real face and a high-quality photograph or a 3D mask of a face.
However, biometric liveness detection is becoming increasingly popular in contact centers too. It provides an advanced layer of security to ensure the authenticity of user interactions. It is typically integrated into a multifactor authentication (MFA) process or as an enhancement to existing security protocols.
Using attack detection vectors, voice biometrics software can be used to create a distinction between actual speakers and audio recordings, or even audio deepfakes. This helps protect against identity spoofing amongst other things.
How Does Biometric Liveness Detection Work?
Depending on the biometric modality, there are various liveness detection techniques that can be used. Here’s a brief overview of how biometric liveness detection works:
Voice biometric liveness detection is specifically tailored to ensure that the voice data presented during an authentication process is genuine and not a recording or synthetic voice. Recognizing and verifying a live voice, as opposed to a playback or synthesized voice, involves a combination of sophisticated techniques. These include:
Detecting background noise: Features like the Signal-to-Noise Ratio (SNR) can be utilized to differentiate between genuine and spoofed samples when analyzing consistency in background noises.
Analyzing natural variability: A genuine voice has slight variations, even when someone tries to repeat the same phrase identically. By analyzing these natural fluctuations in pitch, tone, cadence, and other voice characteristics, the system can determine the authenticity of the voice.
Spectral analysis: A voice signal can be decomposed into its constituent frequencies using Fourier Transform or a Wavelet Transform. Live voices and recordings might exhibit differences in their spectral characteristics, especially if the recording is played back through a secondary medium, like a speaker or a phone. Spectograms can be used to detect anomalies in recorded voices.
Temporal features: Time-domain features like Zero-Crossing Rate (ZCR) and energy contours can provide valuable information. For instance, the ZCR in a recorded voice played back through speakers might differ from a live voice.
Cepstral analysis: Mel-Frequency Cepstral Coefficients (MFCCs) are commonly used in speech and audio processing. Differences in MFCC values between genuine and spoofed voices can be indicators of non-liveness.
Deep learning and neural networks: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) can be trained on large datasets containing both genuine and spoofed voice samples. Once trained, these networks can predict with high accuracy whether a given voice sample is live or spoofed.
Biometric liveness detection is also used for facial recognition. It can analyze patterns related to natural eye blinking, depth perception, or even the natural response to external stimuli, such as light, to determine the presence of a live person.
For companies looking to shore up their defense strategy against deepfakes, using liveness detection techniques is extremely important.
The Benefits of Biometric Liveness Detection for Call Centers
Biometric liveness detection offers many advantages for call centers, including enhanced security and user experience, and greater operational efficiency for banks and other businesses.
Improved Security and Fraud Detection
We’ve already discussed how bad actors and cybercriminals are becoming increasingly sophisticated. In most cases, traditional security measures just don’t cut it anymore.
Biometric liveness detection ensures that the voice or facial data presented is from a genuine, live person and not a recording or synthetic reproduction.
By adding this layer of authentication, call centers can drastically reduce the potential for unauthorized access or fraudulent activities, ensuring the security of both the company and its clients.
Faster, More Efficient Authentication Process
Contact centers today are all about efficiency. Agents are trained to process and handle as many calls as possible, with many using specific KPIs to track productivity.
But, if you’ve ever called your bank, you probably know just how arduous the authentication process can be. Traditional authentication methods included asking for a T-PIN or requiring customers to answer a series of verification questions.
As you can imagine, requiring customers to remember multiple passwords or answer an array of security questions can be cumbersome and time-consuming. Biometric liveness detection offers a faster and more intuitive authentication process.
All a person has to do is speak, and the software uses voice profiling and recognition to determine if the person on the other end is actually genuine or not. Banking fraud detection technologies are quite sophisticated, which is why they require such extensive authentication.
Improved Customer Experience
The benefits outlined above ultimately culminate in a significantly improved customer experience. In today’s world, people value quick and hassle-free interactions.
By integrating liveness detection, call centers can provide an authentication method that’s not only secure but also user-friendly. Eliminating the need for customers to recall passwords or PINs, or answer multiple security questions, results in a smoother and more positive customer experience.
Operational Cost Savings
While the initial investment in biometric liveness detection technology might seem substantial, the long-term cost savings can be significant.
By reducing call times through quicker authentication and cutting down on fraud-related losses, call centers can achieve substantial operational cost reductions. Additionally, the reduction in fraud also means fewer resources spent on fraud investigation and resolution.
Fostering Customer Trust
Data breaches or different types of fraud attempts can ruin a company’s goodwill and have a significant impact on their bottom line.
Companies that demonstrate a commitment to advanced security measures can strengthen their reputation and trustworthiness in the eyes of customers. By adopting biometric liveness detection, call centers convey that they prioritize customer security, which can bolster brand loyalty and trust.
Pindrop Helps Companies Improve Security and Efficiency
Pindrop’s Deep Voice™ Biometric Engine is the leader in voice recognition biometrics. The platform makes it easy to detect fraudsters and ensure maximum security with neural network-based voice recognition.
It allows companies to match a caller’s voice to an established voiceprint, confirming their identity immediately. Its advanced anomaly detection system ensures that any playback tricks or voice alterations are detected, thus ensuring interactions with actual humans. Request a demo today to learn more.