|farmhash||99.98 kB||Apache-2.0||9 Years||4 Feb 2023|
|clientjs||313.94 kB||Apache-2.0||8 Years||25 Oct 2021|
|talisman||142.69 kB||MIT||7 Years||21 Jan 2021|
|gulp-buster||9.79 kB||WTFPL||9 Years||15 Aug 2016|
|broccoli-asset-rev||6.1 kB||MIT||9 Years||13 Aug 2018|
|@fingerprintjs/fingerprintjs||180.66 kB||BUSL-1.1||3 Years||19 Sep 2023|
|ssh2shell||28.95 kB||ISC||9 Years||15 May 2023|
|cordova-plugin-fingerprint-aio||17.45 kB||MIT||7 Years||22 Dec 2021|
|@fingerprintjs/fingerprintjs-pro||10.98 kB||SEE LICENSE IN LICENSE||3 Years||6 Jul 2023|
|static-expiry||6.58 kB||MIT||10 Years||4 Apr 2015|
|gulp-fingerprint||4.83 kB||MIT||9 Years||21 Aug 2019|
|express-fingerprint||5.13 kB||MIT||7 Years||21 Jul 2020|
|fpcalc||196.11 kB||Public Domain||10 Years||18 Oct 2018|
|cordova-plugin-android-fingerprint-auth||369.47 kB||Apache 2.0||7 Years||31 Oct 2018|
|react-native-fingerprint-scanner||17.21 kB||MIT||6 Years||20 Jun 2020|
Biometrics libraries are useful when you’re developing a system that needs to authenticate or verify a user's identity based on one or more unique physical or behavioral characteristics. This can be in a variety of contexts, such as security systems, mobile apps with fingerprint login, face recognition for attendance systems, or identity verification in law enforcement.
In the Node.js domain, these libraries can be utilized to interact with external hardware, process raw biometric data, or interact with third-party services that provide biometric verification.
Given the complex nature of the coding involved in recognizing and interpreting biometric data, npm packages can save developers significant time and effort. These libraries handle the heavy lifting, allowing you to focus on the broader application functionality.
In terms of functionalities, biometrics libraries tend to offer a range of features catered towards different modality types. The most common modalities are fingerprints, face, and iris, but many also handle less common ones like voice, gait, and even typing rhythm.
Basic functionalities that you would expect from a biometric library include:
Data Capture: This feature provides the ability to interact with hardware (like fingerprint sensors or cameras) or software (such as mobile OS APIs) to collect the biometric data.
Data Processing: After data collection, the libraries usually provide functions to clean and preprocess the raw data. This might include steps like normalization, noise reduction, and segmented analysis.
Feature Extraction: Once the data is preprocessed, biometrics libraries can extract meaningful features that can be used for comparison. These often involve complex mathematical operations and machine learning algorithms.
Comparison and Matching: Libraries may also offer functionality to compare the extracted features with pre-registered biometric data, returning a score or a binary match/non-match result.
Here are some gotchas to watch out for when working with biometric libraries:
Hardware Compatibility: Not all libraries are compatible with all biometric data capture devices. It's crucial to ensure that the library you're using supports the specific hardware you intend to use.
Privacy Concerns: Handling biometric data brings in a host of privacy considerations. You need to check if the library provides enough encryption and security facilities to safely store, transmit, and process the biometric data.
Performance: The implementation of algorithms differs amongst libraries and can significantly impact the performance of your application. Be careful in choosing libraries that have been properly optimized.
False Positives and Negatives: No biometric library is perfect, and the accuracy of identification can vary. Make sure to thoroughly test the libraries in real-world conditions and understand their limitations.
Depreciation and Maintenance: Always check whether the libraries are actively maintained and updated. Biometric technology is rapidly evolving, and your application might suffer if the libraries get depreciated or stop receiving updates.