Home Energy Security Prototype using Microcontroller Based on Fingerprint Sensor

The globalization era brings rapid development in technology.The human need for speed and easiness pushed them toinnovate, such as in the security field. Initially, the securitysystem was conducted manually and impractical compared tonowadays system. A security technology that is developed wasbiometric application, particularly fingerprint. Fingerprintbasedsecurity became a reliable enough system because of itsaccuracy level, safe, secure, and comfortable to be used ashousing security system identification. This research aimed todevelop a security system based on fingerprint biometric takenfrom previous researches by optimizing and upgrading theprevious weaknesses. This security system could be a solutionto a robbery that used Arduino UNO Atmega328P CH340 R3Board Micro USB port. The inputs were fingerprint sensor, 4x5keypad, and magnetic sensor, whereas the outputs were 12 Vsolenoid, 16x2 LCD, GSM SIM800L module, LED, andbuzzer. The advantage of this security system was its ability togive a danger sign in the form of noise when the systemdetected the wrong fingerprint or when it detects a forcedopening. The system would call the homeowner then. Otherthan that, this system notified the homeowner of all of theactivities through SMS so that it can be used as a long-distanceobservation. This system was completed with a push button toopen the door from the inside. The maximum fingerprints thatcould be stored were four users and one admin. The admin’sjob was to add/delete fingerprints, replace the home owner’sphone number, and change the system’s PIN. The resultsshowed that the fingerprint sensor read the prints in a relativelyfast time of 1.136 seconds. The average duration that wasneeded to send an SMS was 69 seconds while through call was3.2 seconds

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others