<p>Simply, yes, because you want to use train dlib's object detector which requires a labeled (up to a bounding box) dataset or you will use a available and labeled dataset. </p>

<p>And also the main function of imglab is creating bounding boxes and it's written in your comments:</p>

<blockquote>
  <p>To create your own XML files you can use the imglab tool which can be
  found in the tools/imglab folder.  It is a simple graphical tool for
  labeling objects in images with boxes.</p>
</blockquote>

<p>For the original paper please refer to:
<a href="https://arxiv.org/pdf/1502.00046v1.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1502.00046v1.pdf</a></p>

<p>Actually, as you said, it is really hard. The one of main challenges in object detection or recognition is creating the dataset. That's why, researchers use <code>Mechanical Turk</code> like sites to use the power of crowd. </p>
Training D-lib images
<p>I am using D-lib library to use ocular recognition. So I am planning to train my own classifier using the options given in the documentation. I am using Python as a language platform when compared to C++.</p>

<p>So, I have created the two .xml files training and testing using the imglab tool. Do I have to label all the subject names in the imglab tool?
I have close to 20000 images. Is it not going to be difficult?
Do we have an easy way of doing it? 
Please find the code matching the scenario attached.</p>

<pre><code>import os
import sys
import glob

import dlib
from skimage import io


# In this example we are going to train a face detector based on the small
# faces dataset in the examples/faces directory.  This means you need to supply
# the path to this faces folder as a command line argument so we will know
# where it is.

faces_folder = "/media/praveen/SSD/NIVL_Ocular/NIR_Ocular_Training"


# Now let's do the training.  The train_simple_object_detector() function has a
# bunch of options, all of which come with reasonable default values.  The next
# few lines goes over some of these options.
options = dlib.simple_object_detector_training_options()
# Since faces are left/right symmetric we can tell the trainer to train a
# symmetric detector.  This helps it get the most value out of the training
# data.
options.add_left_right_image_flips = False
# The trainer is a kind of support vector machine and therefore has the usual
# SVM C parameter.  In general, a bigger C encourages it to fit the training
# data better but might lead to overfitting.  You must find the best C value
# empirically by checking how well the trained detector works on a test set of
# images you haven't trained on.  Don't just leave the value set at 5.  Try a
# few different C values and see what works best for your data.
options.C = 5
# Tell the code how many CPU cores your computer has for the fastest training.
options.num_threads = 4
options.be_verbose = True


training_xml_path = os.path.join(faces_folder, "/media/praveen/SSD/NIVL_Ocular/praveen_ocular_dataset.xml")
testing_xml_path = os.path.join(faces_folder, "/media/praveen/SSD/NIVL_Ocular/praveen_ocular_test_dataset.xml")
# This function does the actual training.  It will save the final detector to
# detector.svm.  The input is an XML file that lists the images in the training
# dataset and also contains the positions of the face boxes.  To create your
# own XML files you can use the imglab tool which can be found in the
# tools/imglab folder.  It is a simple graphical tool for labeling objects in
# images with boxes.  To see how to use it read the tools/imglab/README.txt
# file.  But for this example, we just use the training.xml file included with
# dlib.
dlib.train_simple_object_detector(training_xml_path, "detector.svm", options)



# Now that we have a face detector we can test it.  The first statement tests
# it on the training data.  It will print(the precision, recall, and then)
# average precision.
print("")  # Print blank line to create gap from previous output
print("Training accuracy: {}".format(
    dlib.test_simple_object_detector(training_xml_path, "detector.svm")))
# However, to get an idea if it really worked without overfitting we need to
# run it on images it wasn't trained on.  The next line does this.  Happily, we
# see that the object detector works perfectly on the testing images.
print("Testing accuracy: {}".format(
    dlib.test_simple_object_detector(testing_xml_path, "detector.svm")))




#
# # Now let's use the detector as you would in a normal application.  First we
# # will load it from disk.
# detector = dlib.simple_object_detector("detector.svm")
#
# # We can look at the HOG filter we learned.  It should look like a face.  Neat!
# win_det = dlib.image_window()
# win_det.set_image(detector)
#
# # Now let's run the detector over the images in the faces folder and display the
# # results.
# print("Showing detections on the images in the faces folder...")
# win = dlib.image_window()
# for f in glob.glob(os.path.join(faces_folder, "*.png")):
#     print("Processing file: {}".format(f))
#     img = io.imread(f)
#     dets = detector(img)
#     print("Number of faces detected: {}".format(len(dets)))
#     for k, d in enumerate(dets):
#         print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
#             k, d.left(), d.top(), d.right(), d.bottom()))
#
#     win.clear_overlay()
#     win.set_image(img)
#     win.add_overlay(dets)
#     dlib.hit_enter_to_continue()
</code></pre>