Face Recognition using Dlib and Tensorflow

The program is divided into 6 modules:

1, FiletoNumpy.py

	The database initially had labels only for the detected regions of faces which is not enough for the task of recognition.
	Therefore the database has to be labelled manually in a text file before proceeding with face recognition. 
	The following module obtains a numpy array from the text file annotation of the database.

2, facemodel.py

	Loads resnet model, face descriptor and detector from the data folder and return face embedding and num of faces 
	detected.The python file is used as a helper.

3, knnModel.py

	Contains code for a knn model written in Tensorflow. It can function with query descriptor or can operate with 
	test,train data during training time.

4, main.py

	Manages all the tasks of training and checking accuracy for the test/train split. The program has to be run
	whenever a new person has been added into the database.The vectors and labels of new person are stored data folder

5, util.py

	Helper module that contains most of I/O tasks. This helps keep the code clean and relevant.

6, classifier.py

	Should be run in production mode in a loop. The module can detect faces from given image (provided person is in database) 
	and manages scenarios of multiple face detections as well.

Steps for adding new person to Database:

Add 5+ images of person with a front pose in the database [ Dataset/]

Add the name of the image file (.jpg) into faceLabels.txt along with a label number [data/]

	For example:  If the person's images are sanjeev1.jpg,sanjeev2.jpg...
					Add the following lines into faceLabels.txt: 
								............. , .....
								sanjeev1.jpg, 25
								sanjeev2.jpg, 25 
					Here sanjeevN.jpg is the name of the image which will be read
							25 is the id associated with the person

Delete (or backup) Ximages.npy, Ylabels.npy, knnData.npy from the previous database from [data/]

Run FileToNumpy.py

Run main.py

	This will the updated database : Ximages.npy, Ylabels.npy, knnData.npy in [data/]

For inference: Run classifier.py , change path of test to any of the images in the database