Face-Recognition-Login.png' alt='Face Recognition Login Software' title='Face Recognition Login Software' />Face Detection and Recognition in C using Emgu. CV 3. 0 Open. CV Wrapper Part 2 Ahmeds Blog. Tweet. A quick recap. In the first part of this article, we saw how we can initialize our default camera, get image frames from it and carry out face detection. To complete the scope of this article, we would be taking a step further. We would look at how to store training data in a structured database, train your face recognition engine and use to engine to predict faces detected in images. Store Training Data Using SQLiteThe storage of training data would be demonstrated using SQLite due to the fact that it is easy to use. SQLite is a software library that implements a self contained, serverless, zero configuration, transactional SQL database engine. It removes the hassle of client server communication when it comes to using databases. Although it should be pointed out that this procedure can work for all types of database. To create and manage my SQLite database, i make use of Navicat Premium although there are other free alternatives located at this page SQLite Management Tools, feel free to use any of them. The structure of the database used in this article is pretty simple, it contains one user defined table which is named faces and a system generated table called sqlitesequence which SQLite engine uses to keep track of the table IDs. The faces tables holds the training data from the detected images and the corresponding user details associated with them. Below is a screen shot of the table structure. Faces Table Face Recognition Tutorial. From the image above, you can see four table fields. Primary Key, keeps all the records uniquely identified and it is auto incremented starting from 1username A friendly name to associated with the face. Sample The detected and captured face is converted from Image lt Gray, byte format to a byte format and stored in this field. Notice the field is declared a BLOB data type. Id This contains integer values identifying the face. It works like the username, but the recognition engine in Emgu. CV associates numbers as labels with Image rather than strings, hence we need Integer identifiers for the faces for the engine, and username identifier for the users to understand. If this isnt clear yet, we would see in a minute. Having setup our database structure as we want it, we can proceed to code away Storing the recognized face. Foremost, you need the ADO. Most of us arent going to shell out for an iPhone X, but we can still log in with our faces. Unlike passwords, your face is uniquea simple fact that may make it. FingerTec is providing fingerprint, face card time attendance access control solution worldwide. A quick recap In the first part of this article, we saw how we can initialize our default camera, get image frames from it and carry out face detection. To complete. KeyLemon Oasis software library enables fast, scalable and secure face recognition on mobile device. The library has been designed for rapid development of mobile. Lake Worth Drivers License Office Hours. HR Support Center HR AnswerLink Click here TR5 Employee Web Login Click here TR5 Manager Web Login Click here TR365 Employee, Manager, and Partner Web Login Click here. NET Data Provider for SQLite before you can communicate to your SQLite database in C so you would have to add the System. Data. SQLite reference. While this article isnt focusing on the database operations, i would just want to take a minute to discuss how the storage of the recognized face data and other associating details is handled. Face Recognition Login Software' title='Face Recognition Login Software' />Psychology experiments about preferences for faces and voices. Create your family tree and discover your family history. Free genealogy software. Get automatic Smart Matches on over 2. Apple is rolling out facial recognition to unlock its new iPhone X, as has been rumored for months and confirmed in leaks over the weekend. For simplicity sake, i created an interface that has the basic methods i need. Here is the interface declaration. IData. Store. Access. String Save. FaceString username, Byte face. Blob. Listlt Face Call. FacesString username. Is. Username. ValidString username. String Save. AdminString username, String password. Solarwinds Engineers Toolset V10 on this page. Delete. UserString username. Get. User. IdString username. Generate. User. Id. String Get. Usernameint user. Id. Listlt String Get. All. Usernames. The methods defined in the interface are implemented in a separate class. The methods handle the CRUD Create, Read, Update and Delete operations that involve our database. From the method names, the functions are quite understandable. I also have a Face class defined that represents a face object. When i need to select faces from my database, the Call. Faces method defined in the interface class returns a list of face objects that i can easily work with. See the attached at the end of this post implemented class named Data. Store. Access. cs for details on how i implemented the methods in the interface classes. Face. public byte Image get set. Id get set. String Label get set. User. Id get set. Now, the question is how do we get the detected face passed to the Save. Face method in our data access class. Below shows the code that does that. The code below also handles the conversion of the captureddetected face to a byte format suitable for our defined table structure. To. Save new Imagelt Gray, byte pic. Captured. User. Image. Bitmap. Byte file. IData. Store. Access data. Store new Data. Store. Accessdatabase. Path. Save. Dialog new Frm. Save. Dialog. if frm. Save. Dialog. Show. Dialog Dialog. Result. OK. if frm. Save. Dialog. identification. Number. Trim String. Empty. var username frm. Save. Dialog. identification. Number. Trim. To. Lower. var file. Path Application. Startup. Path String. Format0. bmp, username. To. Save. To. Bitmap. Savefile. Path. File. Streamfile. Path, File. Mode. Open, File. Access. Read. using var reader new Binary. Readerstream. Read. Bytesint stream. Length. Store. Save. Faceusername, file. Message. Box. Showresult, Save Result, Message. Box. Buttons. OK. The method above can be attached to a user triggered event e. After a face is detected, the user clicks a save button that calls this method. It can also be automatically called when a face detection happens. The code above also displays a dialog box prompting the user to supply a username for the face that is about to be saved. See previous post on how to do face detection. To make the training and recognition engine work better, we need to capture multiple faces of the same user. Our username is not a key in our database table, so we can have multiple records with the same username. Having multiple faces of a user helps the engine get trained better. Like i mentioned in the previous post, Face recognition is field in Machine learning and as we all know, machine learning greatly relies on a lot of existing data in order to make accuratenear accurate predictions in the future. Training the recognition engine. After having saved our data, we need to train our recognition engine with the dataset we have available. The time at which this takes to complete a training sequence is determined by how large your dataset is. Below shows the code that trains our recognizer engine. Train. Recognizer. Faces data. Store. Access. Call. FacesALLUSERS. Faces null. var face. Images new Imagelt Gray, byte all. Faces. Count. var face. Labels new intall. Faces. Count. for int i 0 i lt all. Faces. Count i. Stream stream new Memory. Stream. stream. Writeall. Facesi. Image, 0, all. Facesi. Image. Length. Image new Imagelt Gray, byte new Bitmapstream. Imagesi face. Image. Resize1. 00,1. Inter. Cubic. face. Labelsi all. Facesi. User. Id. Recognizer. Trainface. Images, face. Labels. Recognizer. Saverecognizer. File. Path. return true. What we did up there is pretty straightforward. We declared a new instance of Eigen. Face. Recognizer which is an Emgu. CV class as face. Recognizer, then we proceeded to call all the faces we have stored in our database as Listlt Face. Each of the faces were converted to Image lt Gray, byte and resized and passed to the Train method of the Eigen. Face. Recognizer class. The Train method takes two main parameters the first is the Array of Images, and the second is the Array of Integer Labels of the images.