ADT offers Yale Assure locks with its ADT home security systems, and now the security service provider has partnered with Yale and the Z-Wave Alliance to introduce the Yale Assure Lock 2 Touch with Z-Wave. This is the first Z-Wave lock with fingerprint recognition that is certified to use the Z-Wave User Credential Command Class specification that was released in June 2024. The new lock also features the latest generation Z-Wave 800 chipset, which promises longer battery life and improved range on a Z-Wave mesh network. Thanks to its use of the Z-Wave User Credential Command Class spec, ADT subscribers will be able arm and disarm their security system at the same time they lock or unlock the new deadbolt, all by just touching their previously enrolled finger to the new lock. ADT offers the Yale Assure Lock 2 Touch with Z-Wave with its ADT home security systems, which can be self- or professionally installed.

Fingerprint recognition remains one of the most reliable biometric technologies due to its high accuracy and uniqueness. Traditional systems rely on contact-based scanners, which are prone to issues such as image degradation from surface contamination and inconsistent user interaction. To address these limitations, contactless fingerprint recognition has emerged as a promising alternative, providing non-intrusive and hygienic authentication. This study evaluates the impact of image enhancement tech-niques on the performance of pre-trained deep learning models using transfer learning for touchless fingerprint recognition. The IIT-Bombay Touchless and Touch-Based Fingerprint Database, containing data from 200 subjects, was employed to test the per-formance of deep learning architectures such as VGG-16, VGG-19, Inception-V3, and ResNet-50. Experimental results reveal that transfer learning methods with fingerprint image enhance-ment (indirect method) significantly outperform those without enhancement (direct method). Specifically, VGG-16 achieved an accuracy of 98% in training and 93% in testing when using the enhanced images, demonstrating superior performance compared to the direct method. This paper provides a detailed comparison of the effectiveness of image enhancement in improving the accuracy of transfer learning models for touchless fingerprint recognition, offering key insights for developing more efficient biometric systems.

The rapid progress of deep speech synthesis models has posed significant threats to society such as malicious content manipulation. Therefore, many studies have emerged to detect the so-called deepfake audio. However, existing works focus on the binary detection of real audio and fake audio. In real-world scenarios such as model copyright protection and digital evidence forensics, it is needed to know what tool or model generated the deepfake audio to explain the decision. This motivates us to ask: Can we recognize the system fingerprints of deepfake audio? In this paper, we present the first deepfake audio dataset for system fingerprint recognition (SFR) and conduct an initial investigation. We collected the dataset from the speech synthesis systems of seven Chinese vendors that use the latest state-of-the-art deep learning technologies, including both clean and compressed sets. In addition, to facilitate the further development of system fingerprint recognition methods, we provide extensive benchmarks that can be compared and research findings. The dataset will be publicly available. .

One of the most challenging problems in fingerprint recognition continues to be establishing the identity of a suspect associated with partial and smudgy fingerprints left at a crime scene (i.e., latent prints or fingermarks). Despite the success of fixed-length embeddings for rolled and slap fingerprint recognition, the features learned for latent fingerprint matching have mostly been limited to local minutiae-based embeddings and have not directly leveraged global representations for matching. In this paper, we combine global embeddings with local embeddings for state-of-the-art latent to rolled matching accuracy with high throughput. The combination of both local and global representations leads to improved recognition accuracy across NIST SD 27, NIST SD 302, MSP, MOLF DB1/DB4, and MOLF DB2/DB4 latent fingerprint datasets for both closed-set (84.11%, 54.36%, 84.35%, 70.43%, 62.86% rank-1 retrieval rate, respectively) and open-set (0.50, 0.74, 0.44, 0.60, 0.68 FNIR at FPIR=0.02, respectively) identification scenarios on a gallery of 100K rolled fingerprints. Not only do we fuse the complimentary representations, we also use the local features to guide the global representations to focus on discriminatory regions in two fingerprint images to be compared. This leads to a multi-stage matching paradigm in which subsets of the retrieved candidate lists for each probe image are passed to subsequent stages for further processing, resulting in a considerable reduction in latency (requiring just 0.068 ms per latent to rolled comparison on a AMD EPYC 7543 32-Core Processor, roughly 15K comparisons per second). Finally, we show the generalizability of the fused representations for improving authentication accuracy across several rolled, plain, and contactless fingerprint datasets.

Ask Pixel 6 owners about their top gripe and they'll likely point to the slow, finicky fingerprint sensor. There may be an explanation for that momentary anguish, though. Google is telling users that the Pixel 6's fingerprint reader is using "enhanced security algorithms" that may either take longer to check your digits or require better sensor contact. We've asked Google for comment. Some users have suggested the sluggish performance might be due to Google's use of an optical under-display fingerprint reader instead of the ultrasonic sensor found in phones like the Galaxy S21. However, Reddit users noted there are phones with optical sensors that perform faster, such as the OnePlus 9. There's a real chance software may play a role in the Pixel 6's quirks.

WhatsApp's recently introduced security feature could be the key to keeping secret messages safe and secure. The company recently unveiled biometric tools in the iPhone version of the app which mean that the phone will check you're the right person before allowing you in. The setting means that you can only open WhatsApp if you have the right fingerprint or face, just like when you unlock your phone. It means that anyone who shares their phone around or is likely to have it unlocked can keep messages secret, even if other apps aren't locked up. The feature involves a slight trade-off: it's much harder for anyone to get into your chats, but it's also a little harder for you to do so, too.

In the past few weeks, you may have noticed a mysterious message popping up on your iPhone after hours of non-use. A seemingly new prompt requires users to enter a passcode to access their phone, even though they have Touch ID enabled – but only if it hasn't been unlocked using its passcode in six days, and the Touch ID hasn't been used within the last eight hours. Though Apple has said the feature was added with the release of iOS 9, users have just now begun to see it, causing many to speculate about its connection to the firm's recent tensions with the FBI. A seemingly new prompt requires users to enter a passcode to access their phone, even though they have Touch ID enabled – but only if it hasn't been unlocked using its passcode in six days, and the Touch ID hasn't been used within the last eight hours According to the iOS Security Guide published earlier this month, there are a number of situations in which you may have to use your passcode to unlock your iPhone or iPad even if Touch ID is enabled. According to Macworld, the message reads'The passcode has not been used to unlock the device in the last six days and Touch ID has not unlocked the device in the last eight hours.'

LG Innotek has developed a cover glass for smartphones that has fingerprint recognition module embedded in it, the company said. The sensor is more sensitive than those currently in the market and does not require a separate sensor -- most of the market's fingerprint sensors on smartphones are a circular or square-shaped sensor "button" that requires direct contact with the user's fingers. The sensor fits in a 0.3 millimetre-deep space at the backside of the bottom of the glass, allowing it to read fingerprints if the user touches the glass covering it. The cover glass will allow smartphone manufacturers to develop lean products that don't require the sensor to stick out. The sensor embedded in the glass will also make it easier for manufacturers to design phones to be water- and dust-proof, the company said.