The report explores biometric applications across many industry use cases

Featuring thought leadership from 30 of our supplier members, this report offers real-world insights and tested solutions. Submissions explore the ethical and responsible use of biometrics, both present and future, offering practical approaches to navigating the complexities of this rapidly advancing technology.

The report also highlights the importance of rigorous testing and showcases real-world use cases that provide valuable perspectives on ensuring biometric technologies respect privacy and build public trust in the technology. It addresses the growing demand for transparency and accountability in biometric systems, demonstrating how responsible development and deployment can achieve this.

Themes across the report include:

• AI
• Digital identity
• Demographic differentials and inclusion
• Data protection, ethics and privacy
• Standards and testing
• Education and training
• Remote identity and liveness
• Biometric capture
• Deepfakes and vulnerabilities
• Seamless travel and borders
• Finance
• Multimodal biometrics
• Governance and legislation

The Biometrics Institute does not endorse any of the submissions in the report but is sharing them for information purposes and to help generate discussion.

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The submissions to the report include insight into deploying biometrics ethically and responsibly, protecting data, mitigating the risks of attack and identity theft, and using biometrics and digital identity to facilitate border crossings and access solutions.

Sixteen member organisations submitted papers outlining concepts and solutions ranging from digital identity authentication and fraud prevention, to recognising morphing attacks and liveness detection.  

The Biometrics Institute does not endorse any of the submissions in the report but is sharing them for information purposes and to help generate discussion.

You may also be interested in our Digital Onboarding and Biometrics paper. Find out more here.

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As outlined in the State of Biometrics Report, touchless recognition is getting a boost due to COVID-19. Of course, face recognition is naturally touchless and many systems are using face. For fingerprint recognition, non-contact fingerprint systems include those which involve a photograph of the fingers, taken with a mobile device. There are also systems which are a kiosk where an individual holds or waves their hand across a sensor. Continued standards development and expanded testing is underway as this technology matures.

To read the full report, become a member. Details on membership benefits and how to apply can be found here.

COVID-19 recovery | Digital identity | Governance | Commercial use of biometrics | In the news

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Read the 2020 report here.

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2015 Survey Summary

2015 Full Report

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Soft biometric modalities have shown their utility in different applications including reducing the search space significantly. This leads to improved recognition performance, reduced computation time, and faster processing of test samples. Some common soft biometric modalities are ethnicity, gender, age, hair color, iris color, presence of facial hair or moles, and markers. This research focuses on performing ethnicity and gender classification on iris images. We present a novelsupervised autoencoder based approach, Deep Class-Encoder, which uses class labels to learn discriminative representation for the given sample by mapping the learned feature vector to its label. The proposed model is evaluated on two datasets each for ethnicity and gender classification. The results obtained using the proposed Deep Class-Encoder demonstrate its effectiveness in comparison to existing approaches and state-of-the-art methods.

Gender and Ethnicity Classification of Iris Images using Deep Class-Encoder

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In this paper, we study the problem of Face Recognition (FR) when using Single Sensor Multi-Wavelength (SSMW) imaging systems that operate in the Short-Wave Infrared (SWIR) band. The contributions of our work are four fold: First, a SWIR database is collected when using our developed SSMW system under the following scenarios, i.e. Multi-Wavelength (MW) multi-pose images were captured when the camera was focused at either 1150, 1350 or 1550 nm. Second, an automated quality-based score level fusion scheme is proposed for the classification of input MW images. Third, a weighted quality-based score level fusion scheme is proposed for the automated classification of full frontal (FF) vs. nonfrontal (NFF) face images. Fourth, a set of experiments is performed indicating that our proposed algorithms, for the classification of (i) multiwavelength images and (ii) FF vs. NFF face images, are beneficial when designing different steps of multi-spectral face recognition (FR) systems, including face detection, eye detection and face recognition. We also determined that when ourSWIR-based system is focused at 1350 nm, the identification performance increases compared to focusing the camera at any of the other SWIR wavelengths available. This outcome is particularly important for unconstrained FR scenarios, where imaging at 1550 nm, at long distances and when operating at night time environments, is preferable over different SWIR wavelengths.

Face recognition in the SWIR band when using single sensor multi-wavelength imaging systems

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Improving Presentation Attack Detection in Dynamic Handwritten Signature Biometrics

The post IMPROVING PRESENTATION ATTACK DETECTION IN DYNAMIC HANDWRITTEN SIGNATURE BIOMETRICS appeared first on Biometrics Institute.

Biometrics Institute Industry Tracker 2017

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