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Easy Biometric sensors for Arduino

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Biometric Sensors for Arduino

Tags: Arduino, biometric sensors

The word biometric is derived from the Greek words bio and metric. Where bio means life and metric means to measure. Biometrics are used to identify his or her physical and behavior characteristics of a person. This method of identification is chosen over traditional methods, including PIN numbers and passwords for its exactness and case sensitiveness. Based on the designing, this system can be used as an identification system or authentication system. These systems are divided into various types which includes vein pattern, fingerprints, hand geometry, DNA, voice pattern, iris pattern, signature dynamics and face detection. This article discusses about what is biometric sensor, different types of biometric sensors available for Arduino.

Biometric sensors are great solution for your DIY bioengineering projects since most of them are easy to deal with.

What is a biometric sensor?

A biometric sensor is a transducer that changes a biometric treat of a person into an electrical signal. Biometric treats mainly include biometric fingerprint reader, iris, face, voice, etc. Generally the sensor reads or measures light, temperature, speed, electrical capacity and other types of energies.Different technologies can be applied to get this conversation using sophisticated combinations, networks of sensors and digital cameras. Every biometric device requires one type of sensor. The biometrics applications mainly includes: used in a high definition camera for facial recognition or in a microphone for voice capture.Some biometrics are specially designed to scan the vein patterns under your skin. Biometric sensors are an essential feature of identity technology.

Which types of biometric sensor are existing?

Biometric sensors or access control systems are classified into two types such as Physiological Biometrics and Behavioral Biometrics. The physiological biometrics mainly include face recognition, fingerprint, hand geometry, Iris recognition and DNA. Whereas behavioral biometrics include keystroke, signature and voice recognition.For better understanding of this concept,some of them are discussed below.

Fingerprint Recognition

Fingerprint Recognition includes taking a fingerprint image of a person and records its features like arches, whorls, and loops along with the outlines of edges, minutiae and furrows. Matching of the Fingerprint can be attained in three ways, such as minutiae, correlation and ridge

Minutiae based fingerprint matching stores a plane includes a set of points and the set of points are corresponding in the template and the i/p minutiae.

Correlation based fingerprint matching overlays two fingerprint images and association between equivalent pixels is calculated.

Ridge feature based fingerprint matching is an innovative method that captures ridges, as minutiae based fingerprint capturing of the fingerprint images is difficult in low quality.

To capture the fingerprints, present methods employ optical sensors that use a CMOS image sensor or CCD; solid state sensors work on the principle of transducer technology using thermal, capacitive, piezoelectric sensors or electric field ; or ultrasound sensors work on echography in which the sensor sends acoustic signals through the transmitter near the finger and captures the signals in the receiver. Scanning of the fingerprint is very stable and also reliable. It safeguards entry devices for building door locks and access of computer network are becoming more mutual. At present, a small number of banks have initiated using fingerprint readers for approval at ATMs.

Face Recognition

Face recognition system is a one type of biometric computer application which can identify or verify a person from a digital image by comparing and analyzing patterns. These biometric systems are used in security systems. Present facial recognition systems work with face prints and these systems can recognize 80 nodal points on a human face. Nodal points are nothing but end points used to measure variables on a person’s face, which includes the length and width of the nose, cheekbone shape and the eye socket depth.

Face recognition systems work by capturing data for the nodal points on a digital image of a person’s face and resulting data can be stored as a face print. When the conditions are favorable, these systems use a face prints to identify accurately.Currently, these systems focus on smartphone applications which include personal marketing, social networking and image tagging purposes. Social sites like FB uses software for face recognition to tag the users in photographs. This software also increases marketing personalization. For instance, billboards have been designed with integrated software that recognizes the ethnicity, gender and estimated age of onlookers to deliver targeted marketing.

Iris Recognition

Iris recognition is a one type of bio-metric method used to identify the people based on single patterns in the region of ring shaped surrounded the pupil of the eye. Generally, the iris has a blue, brown, gray or green color with difficult patterns which are noticeable upon close inspection. Please follow the below link to know more about iris recognition technology.

Voice Recognition

Voice recognition technology is used to produce speech patterns by combining behavioral and physiological factors that can be captured by processing the speech technology.The most important properties used for speech authentication are nasal tone, fundamental frequency, inflection, cadence. Voice recognition can be separated into different categories based on the kind of authentication domain, such as a fixed text method, in the text dependent method, the text independent method and conversational technique.

Signature Recognition

Signature recognition is a one type of biometric method used to analyze and measure the physical activity of signing like the pressure applied, stroke order and the speed. Some biometrics are used to compare visual images of signatures. Signature recognition can be operated in two different ways, such as static and dynamic.

In static mode, consumers write their signature on paper, digitize it through a camera or an optical scanner. This system identifies the signature examining its shape.

In dynamic mode, consumers write their signature in a tablet which is digitized, that obtains the signature in real time. Another option is the gaining by means of stylus-operated PDAs. Some biometrics also operate with smart-phones with a capacitive screen, where consumers can sign using a pen or a finger. This type of recognition is also known as “on-line”.

Top list of biometric sensors for Arduino

1. Optical fingerprint reader FPM10A or R30X

This is a easy to use fingerprint reader.

Specifications:

  • Supply voltage: 3.6 - 6.0 VDC
  • Operating current: 120mA max
  • Peak current: 150mA max
  • Fingerprint imaging time: <1.0 seconds
  • Window area: 14mm x 18mm
  • Signature file: 256 bytes
  • Template file: 512 bytes
  • Storage capacity: 162 templates
  • Safety ratings (1-5 low to high safety)
  • False Acceptance Rate: <0.001% (Security level 3)
  • False Reject Rate: <1.0% (Security level 3)
  • Interface: TTL Serial
  • Baud rate: 9600, 19200, 28800, 38400, 57600 (default is 57600)
  • Working temperature rating: -20C to +50C
  • Working humidy: 40%-85% RH
  • Full Dimensions: 56 x 20 x 21.5mm
  • Exposed Dimensions (when placed in box): 21mm x 21mm x 21mm triangular
  • Weight: 20 grams

See the project with this sensor on our website.

2. Fingerprint Scanner GT-511C3

The module itself does all of the heavy lifting behind reading and identifying the fingerprints with an on-board optical sensor and 32-bit CPU. All you need to do is send it simple commands. To get started, just register each fingerprint that you want to store by sending the corresponding command and pressing your finger against the reader three times. The fingerprint scanner can store different fingerprints and the database of prints can even be downloaded from the unit and distributed to other modules. As well as the fingerprint "template," the analyzed version of the print, you can also retrieve the image of a fingerprint and even pull raw images from the optical sensor!

This is the updated version of the GT-511 which has an increased memory capacity. The module can store up to 200 different fingerprints (that's 10x more than the old version!) and is now capable of 360° recognition.

The module is small and easy to mount using two mounting tabs on the side of the sensor. The on-board JST-SH connector has four signals: Vcc, GND, Tx, Rx. A compatible JST-SH pigtail can be found in the related items below. Demo software for PC is available in the documents below, simply connect the module to your computer using an FTDI Breakout and start the software to read fingerprints!

The module does not come with a cable, if you do not have a 4-wire JST-SH pigtail, you can add PRT-10359

Dimensions: 37 x 17 x 9.5 mm

Features:

  • High-Speed, High-Accuracy Fingerprint Identification using the SmackFinger 3.0 Algorithm
  • Download Fingerprint Images from the Device
  • Read and Write Fingerprint Templates and Databases
  • Simple UART protocol (Default 9600 baud)
  • Capable of 1:1 Verification and 1:N Identification

3. Pulse, Heart Rate Sensor for Arduino

  • Plug and play heart rate sensor for Arduino
  • Can be used by students, artists, athletes, makers, and game & mobile developers
  • Comes with 24 inch color coded Cable with standard male headers
  • Heart rate data can be really useful for designing an exercise routine

The Pulse, Heart Rate Sensor for Arduino is a plug and play heart rate sensor for Arduino. It can be used by students, artists, athletes, makers, and game & mobile developers who want to easily incorporate live heart rate data into their projects. Heart rate data can be really useful whether you're designing an exercise routine, studying your activity or anxiety levels or just want your shirt to blink with your heart beat. It essentially combines a simple optical heart rate sensor with amplification and noise cancellation circuitry making it fast and easy to get reliable pulse readings.

It is very easy to use with your Arduino, as it only requires one analog data pin, VCC and GND. The sensor can be easily powered up using the Arduino 5V pin.

To measure the heart rate, you just need to clip the sensor in your earlobe or fingerprint. There are an example code and a Processing sketch for visual data here.

4. Galvanic skin response grove module

GSR, or "galvanic skin response", is a method of measuring the electrical conductance of the skin. Strong emotion can cause stimulus to your sympathetic nervous system, resulting more sweat being secreted by the sweat glands. Grove – GSR allows you to measure such emotions by simple attaching two electrodes to two fingers on one hand, an interesting gear to create emotion related projects, like sleep quality monitor. GSR measurement devices are also commonly used by "lie detectors".

Specifications:

  • Input Voltage: 5V/3.3V
  • Sensitivity adjustable via a potentiometer
  • External measuring finger cots

Please see the wiki page for more info.

5. Myoware muscle sensor
Using our muscles to control things is the way that most of us are accustomed to doing it. We push buttons, pull levers, move joysticks… but what if we could take the buttons, levers and joysticks out of the equation? This is the MyoWare Muscle Sensor, an Arduino-powered, all-in-one electromyography (EMG) sensor from Advancer Technologies. The MyoWare board acts by measuring the filtered and rectified electrical activity of a muscle; outputting 0-Vs Volts depending the amount of activity in the selected muscle, where Vs signifies the voltage of the power source. It’s that easy: stick on a few electrodes (not included), read the voltage out and flex some muscles!
The MyoWare Muscle Sensor is the latest revision of the Muscle Sensor of old, now with a new wearable design that allows you to attach biomedical sensor pads directly to the board itself getting rid of those pesky cables. This new board also includes a slew of other new features including, single-supply voltage of +3.1V to +5V, RAW EMG output, polarity protected power pins, indicator LEDs, and (finally) an On/Off switch. Additionally, we have developed a few shields (Cable, Power, and Proto) that can attach to the Myoware Muscle Sensor to help increase its versatility and functionality!
Measuring muscle activity by detecting its electric potential, referred to as electromyography (EMG), has traditionally been used for medical research. However, with the advent of ever shrinking yet more powerful microcontrollers and integrated circuits, EMG circuits and sensors have found their way into all kinds of control systems.

Features:

  • Wearable Design
  • Single Supply
  • +2.9V to +5.7V
  • Polarity reversal protection
  • Two Output Modes
  • EMG Envelope
  • Raw EMG
  • Expandable via Shields
  • LED Indicators
  • Specially Designed For Microcontrollers
  • Adjustable Gain
  • 0.82" x 2.06"

See more information here.

6. Finger heart rate sensor KY039

This finger heart rate sensor measures the pulse in your fingers by using an infrared IR LED and an optical transistor.

It easily interfaces with the Arduino using just one data pin. 

7. e-Health shield

The e-Health Sensor Shield v2.0 can be connected to your Arduino, Raspberry Pi or Intel Galileo board for all electronics projects requiring use of reliable biometric measurements. It provides information collected from 9 different biometric sensors.

The biometric e-Health Sensor Shield v2.0 can therefore be used for either monitoring in real time or gathering data for later analysis. The data collected can be transmitted via the shield’s various interfaces (WiFi, GPRS, Bluetooth, 3G, 802.15.4 or ZigBee).

If you wish to use the e-Health Shield as a Raspberry Pi shield, you’ll need the Raspberry Pi to Arduino connection bridge.

Create a complete biometric kit with the e-Health Sensor Shield v2.0 and the e-Health Sensor Platform kit

To make the most of all the e-Health Sensor Shield v2.0 has to offer, there is also a complete e-Health Sensor Platform kit (also available on our website) comprising the 9 sensors supported by the biometric shield for transmitting the following data:

  • Pulse and blood oxygenation
  • Respiration
  • Body temperature
  • Electrocardiogram
  • Blood glucose
  • Skin conductance
  • Blood pressure (new)
  • Acceleration
  • Electromyogram (new)

Please note: the sensors and all the hardware offered here as part of the e-Health Sensor Shield v2.0 must not be used in the place of more appropriate measurements taken for the professional medical follow-up of patients. This shield is above all intended for use in projects carried out by developers, researchers, students and designers. In no case can it be used as a substitute for a medical diagnosis.

Technical specifications of the e-Health Sensor Shield v2.0:

  • RoHS compliant
  • Arduino, Intel Galileo and Raspberry Pi compatible
  • For use with the complete e-Health Sensor Platform kit

8. MQ-3 alcohol sensor

Sensitive material MQ-3 gas sensor is used in clean air low conductivity tin oxide (SnO2). When there is alcohol vapor in the environment sensor, conductivity sensor with increasing gas concentration of alcohol in the air increases. Use simple circuit can convert the change in conductivity of the gas concentration corresponding to the output signal. High MQ-3 gas sensor sensitivity to alcohol, can resist the interference of gasoline, smoke, water vapor. The sensor can detect a variety of alcohol concentration in the atmosphere, is a low-cost sensor suitable for a variety of applications

Application:

For on-site detection of motor vehicle drivers and other non-drinking workers, but also for detecting other places of ethanol vapor, alcohol detection range: detection range 10 ~ 1000ppm.

Features:

  • Using high-quality dual-panel design,with power indicator and TTL signal output instructions.
  • The switching signal having a DO (TTL) output and analog output AO.
  • TTL output valid signal is low. ( Low-level signal when the output light can be directly connected to the microcontroller or relay module )
  • Analog output voltage with the higher concentration of higher voltage
  • There are four screw holes for easy positioning.
  • Has a long life and reliable stability
  • Rapid response and recovery characteristic

Input voltage : DC5V Power consumption ( current ): 150mA

DO output : TTL digital 0 and 1 ( 0.1 and 5V)

AO output :0.1-0 .3 V ( relative to pollution ) , the maximum concentration of a voltage of about 4V

Special note: After the sensor is powered,needs to warm up around 20S,measured data was stable,heat sensor is a normal phenomenon,because the internal heating wire,if hot is not normal .

Wiring:

  • VCC: positive power supply (5V)
  • GND: power supply is negative
  • DO: TTL switching signal output
  • AO: analog signal output

9. TTP223B touch digital sensor

The TTP223B Touch digital sensor is like a  pushbutton. It is a touch-sensing IC capacitive switch module. The sensor outputs LOW, except,  when your finger touches the corresponding sensor position. In this case,  the sensor outputs HIGH. If the sensor is not touched for 12 seconds, it switches to LOW again.

TTP223 is 1 Key Touch pad detector IC, and it is suitable to detect capacitive element variations. It consumes very low power and the operating voltage is only between 2.0V~5.5V. The response time max about 60mS at fast mode, 220mS at low power mode @VDD=3V. Sensitivity can adjust by the capacitance(0~50pF) outside. 

Applications:

  • Water proofed electric products
  • Button key replacement
  • Consumer products
See the project on our website.


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Published at 31-10-2018
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