Using While loop with Queues in Tensorflow - while-loop

Question

I have a set of images which I'm going to feed into a graph in tensorflow. Fetching the data is done through a FIFOQueue. The problem is that in some images, the face is not detected, that is, the image does not contain a face. Therefore, I am going to ignore these images before feeding them into the graph. My code is as follows:
import tensorflow as tf
import numpy as np
num_epoch = 100
tfrecords_filename_seq = ["C:/Users/user/PycharmProjects/AffectiveComputing/P16_db.tfrecords"]
filename_queue = tf.train.string_input_producer(tfrecords_filename_seq, num_epochs=num_epoch, shuffle=False, name='queue')
reader = tf.TFRecordReader()
current_image_confidence = tf.Variable(tf.constant(0.0, dtype=tf.float32))
image = tf.Variable(tf.ones([112, 112, 3]), dtype=tf.float32)
annotation = tf.Variable('', dtype=tf.string)
def body():
key, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'height': tf.FixedLenFeature([], tf.int64),
'width': tf.FixedLenFeature([], tf.int64),
'image_raw': tf.FixedLenFeature([], tf.string),
'annotation_raw': tf.FixedLenFeature([], tf.string)
})
# This is how we create one example, that is, extract one example from the database.
image_ = tf.decode_raw(features['image_raw'], tf.uint8)
# The height and the weights are used to
height = tf.cast(features['height'], tf.int32)
width = tf.cast(features['width'], tf.int32)
# The image is reshaped since when stored as a binary format, it is flattened. Therefore, we need the
# height and the weight to restore the original image back.
image.assign(tf.reshape(image_, [height, width, 3]))
annotation.assign(tf.cast(features['annotation_raw'], tf.string))
current_image_confidence.assign(tf.slice(tf.string_to_number(tf.string_split(annotation, delimiter=','),
out_type=tf.float32),
begin=[0, 3],
size=[1, 1]))
def cond():
tf.equal(current_image_confidence, tf.constant(0.0, dtype=tf.float32))
loop = tf.while_loop(cond, body, [current_image_confidence, reader, image, annotation])
Therefore, I need a while loop that will run until I get an image with face. That is when I need to terminate the loop and send the image to the graph.
Please note that my data is stored in a tfrecord file. So each record contains one image and a set of features called annotation, saved as a tf.string. So the current_image_confidence Variable is used to hold a value of 1 or 0 based whether a face is present or not.
How to fix the code??
Any help is much appreciated!!