BigDataRevealed-Biometrics for GDPR & Beyond unveils V2, in Spark/Java, Amazon AWS S3, Hadoop/HDFS/HBase, using innovative methods & strategies for pre-processing images, increasing matching accuracy, with a GUI to configure the matching algorithm parameters.
- Biometrics for Amazons AWS S3 and Apache/most any, Hadoop.
- Inventive API’s & algorithms that do not use the standard Crop or Rotate methods in images. Increases in accuracy by 10-20%.
- Based on OpenCV, packaged in Spark/Java for AWS S3 & Apache Hadoop; using Hbase as a target for results, greatly increasing scalability & speed of pre-processing and matching.
- Ability to modify & control the main parameters of the LBPH algorithm, while enhancing its matching capabilities.
- Ability to modify pre-processing parameters such as; threshold, scale factor, minimum neighbors, resize face (width, height), lbp radius & sample points, to enhance matching.
- Objects & Facial Recognition on Amazons AWS S3 or Hadoop for source & storage of images.
- Apache HDFS, source & store images & target Hbase
- Crops photos based on specialized algorithm for improved matching accuracy.
- Blur-Filter based mostly on the OpenCV for image cleansing.
#biometrics #facial recognition #gdpr #hadoop #aws #azure