Face Recognition

On this article, we’re going to learn how to detect faces in real-time utilizing OpenCV. After detecting the face from the webcam stream, we’re going to save the frames containing the face. Later we are going to move these frames (photographs) to our masks detector classifier to search out out if the individual is carrying a masks or not.

We’re additionally going to see the right way to make a customized masks detector utilizing Tensorflow and Keras however you’ll be able to skip that as I shall be attaching the skilled mannequin file under which you’ll be able to obtain and use. Right here is the checklist of subtopics we’re going to cowl:

What’s Face Detection?
Face Detection Strategies
Face detection algorithm
Face recognition
Face Detection utilizing Python
Face Detection utilizing OpenCV
Create a mannequin to recognise faces carrying a masks (Non-compulsory)
Find out how to do Actual-time Masks detection

What is Face Detection?

The objective of face detection is to find out if there are any faces within the picture or video. If a number of faces are current, every face is enclosed by a bounding field and thus we all know the situation of the faces

The first goal of face detection algorithms is to precisely and effectively decide the presence and place of faces in a picture or video. The algorithms analyze the visible content material of the information, looking for patterns and options that correspond to facial traits. By using varied strategies, reminiscent of machine studying, picture processing, and sample recognition, face detection algorithms intention to differentiate faces from different objects or background parts inside the visible information.

Human faces are tough to mannequin as there are lots of variables that may change for instance facial features, orientation, lighting circumstances, and partial occlusions reminiscent of sun shades, scarfs, masks, and so forth. The results of the detection provides the face location parameters and it could possibly be required in varied kinds, as an illustration, a rectangle masking the central a part of the face, eye facilities or landmarks together with eyes, nostril and mouth corners, eyebrows, nostrils, and so forth.

Face Detection Strategies

There are two fundamental approaches for Face Detection:

Function Base Strategy
Picture Base Strategy

Function Base Strategy

Objects are often acknowledged by their distinctive options. There are lots of options in a human face, which could be acknowledged between a face and lots of different objects. It locates faces by extracting structural options like eyes, nostril, mouth and so forth. after which makes use of them to detect a face. Usually, some kind of statistical classifier certified then useful to separate between facial and non-facial areas. As well as, human faces have explicit textures which can be utilized to distinguish between a face and different objects. Furthermore, the sting of options may help to detect the objects from the face. Within the coming part, we are going to implement a feature-based strategy by utilizing the OpenCV tutorial.

Picture Base Strategy

Normally, Picture-based strategies depend on strategies from statistical evaluation and machine studying to search out the related traits of face and non-face photographs. The realized traits are within the type of distribution fashions or discriminant features that’s consequently used for face detection. On this technique, we use totally different algorithms reminiscent of Neural-networks, HMM, SVM, AdaBoost studying. Within the coming part, we are going to see how we will detect faces with MTCNN or Multi-Activity Cascaded Convolutional Neural Community, which is an Picture-based strategy of face detection

Face detection algorithm

One of many in style algorithms that use a feature-based strategy is the Viola-Jones algorithm and right here I’m briefly going to debate it. If you wish to learn about it intimately, I might counsel going by this text, Face Detection utilizing Viola Jones Algorithm.

Viola-Jones algorithm is known as after two pc imaginative and prescient researchers who proposed the strategy in 2001, Paul Viola and Michael Jones of their paper, “Fast Object Detection utilizing a Boosted Cascade of Easy Options”. Regardless of being an outdated framework, Viola-Jones is kind of highly effective, and its utility has confirmed to be exceptionally notable in real-time face detection. This algorithm is painfully gradual to coach however can detect faces in real-time with spectacular velocity.

Given a picture(this algorithm works on grayscale photographs), the algorithm seems to be at many smaller subregions and tries to discover a face by searching for particular options in every subregion. It must test many various positions and scales as a result of a picture can comprise many faces of varied sizes. Viola and Jones used Haar-like options to detect faces on this algorithm.

Face detection and Face Recognition are sometimes used interchangeably however these are fairly totally different. In actual fact, Face detection is simply a part of Face Recognition.

Face recognition is a technique of figuring out or verifying the id of a person utilizing their face. There are numerous algorithms that may do face recognition however their accuracy may range. Right here I’m going to explain how we do face recognition utilizing deep studying.

In actual fact right here is an article, Face Recognition Python which exhibits the right way to implement Face Recognition.

Face Detection utilizing Python

As talked about earlier than, right here we’re going to see how we will detect faces by utilizing an Picture-based strategy. MTCNN or Multi-Activity Cascaded Convolutional Neural Community is certainly one of the crucial in style and most correct face detection instruments that work this precept. As such, it’s primarily based on a deep studying structure, it particularly consists of three neural networks (P-Web, R-Web, and O-Web) linked in a cascade.

So, let’s see how we will use this algorithm in Python to detect faces in real-time. First, it is advisable to set up MTCNN library which accommodates a skilled mannequin that may detect faces.

pip set up mtcnn

Now allow us to see the right way to use MTCNN:

from mtcnn import MTCNN
import cv2
detector = MTCNN()
#Load a videopip TensorFlow
video_capture = cv2.VideoCapture(0)
whereas (True):
    ret, body = video_capture.learn()
    body = cv2.resize(body, (600, 400))
    containers = detector.detect_faces(body)
    if containers:
        field = containers[0]['box']
        conf = containers[0]['confidence']
        x, y, w, h = field[0], field[1], field[2], field[3]
        if conf > 0.5:
            cv2.rectangle(body, (x, y), (x + w, y + h), (255, 255, 255), 1)
    cv2.imshow("Body", body)
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break
video_capture.launch()
cv2.destroyAllWindows()

Face Detection utilizing OpenCV

On this part, we’re going to carry out real-time face detection utilizing OpenCV from a reside stream by way of our webcam.

As movies are mainly made up of frames, that are nonetheless photographs. We carry out face detection for every body in a video. So with regards to detecting a face in a nonetheless picture and detecting a face in a real-time video stream, there may be not a lot distinction between them.

We shall be utilizing Haar Cascade algorithm, often known as Voila-Jones algorithm to detect faces. It’s mainly a machine studying object detection algorithm that’s used to establish objects in a picture or video. In OpenCV, now we have a number of skilled Haar Cascade fashions that are saved as XML information. As an alternative of making and coaching the mannequin from scratch, we use this file. We’re going to use “haarcascade_frontalface_alt2.xml” file on this mission. Now allow us to begin coding this up

Step one is to search out the trail to the “haarcascade_frontalface_alt2.xml” file. We do that by utilizing the os module of Python language.

import os
cascPath = os.path.dirname(
    cv2.__file__) + "/information/haarcascade_frontalface_alt2.xml"

The subsequent step is to load our classifier. The trail to the above XML file goes as an argument to CascadeClassifier() technique of OpenCV.

faceCascade = cv2.CascadeClassifier(cascPath)

After loading the classifier, allow us to open the webcam utilizing this easy OpenCV one-liner code

video_capture = cv2.VideoCapture(0)

Subsequent, we have to get the frames from the webcam stream, we do that utilizing the learn() perform. We use it in infinite loop to get all of the frames till the time we wish to shut the stream.

whereas True:
    # Seize frame-by-frame
    ret, body = video_capture.learn()

The learn() perform returns:

The precise video body learn (one body on every loop)
A return code

The return code tells us if now we have run out of frames, which is able to occur if we’re studying from a file. This doesn’t matter when studying from the webcam since we will report perpetually, so we are going to ignore it.

For this particular classifier to work, we have to convert the body into greyscale.

grey = cv2.cvtColor(body, cv2.COLOR_BGR2GRAY)

The faceCascade object has a technique detectMultiScale(), which receives a body(picture) as an argument and runs the classifier cascade over the picture. The time period MultiScale signifies that the algorithm seems to be at subregions of the picture in a number of scales, to detect faces of various sizes.

  faces = faceCascade.detectMultiScale(grey,
                                         scaleFactor=1.1,
                                         minNeighbors=5,
                                         minSize=(60, 60),
                                         flags=cv2.CASCADE_SCALE_IMAGE)

Allow us to undergo these arguments of this perform:

scaleFactor – Parameter specifying how a lot the picture dimension is decreased at every picture scale. By rescaling the enter picture, you’ll be able to resize a bigger face to a smaller one, making it detectable by the algorithm. 1.05 is an effective potential worth for this, which suggests you utilize a small step for resizing, i.e. cut back the scale by 5%, you enhance the prospect of an identical dimension with the mannequin for detection is discovered.
minNeighbors – Parameter specifying what number of neighbors every candidate rectangle ought to need to retain it. This parameter will have an effect on the standard of the detected faces. Larger worth ends in fewer detections however with larger high quality. 3~6 is an effective worth for it.
flags –Mode of operation
minSize – Minimal potential object dimension. Objects smaller than which are ignored.

The variable faces now comprise all of the detections for the goal picture. Detections are saved as pixel coordinates. Every detection is outlined by its top-left nook coordinates and the width and top of the rectangle that encompasses the detected face.

To indicate the detected face, we are going to draw a rectangle over it.OpenCV’s rectangle() attracts rectangles over photographs, and it must know the pixel coordinates of the top-left and bottom-right corners. The coordinates point out the row and column of pixels within the picture. We are able to simply get these coordinates from the variable face.

for (x,y,w,h) in faces:
        cv2.rectangle(body, (x, y), (x + w, y + h),(0,255,0), 2)

rectangle() accepts the next arguments:

The unique picture
The coordinates of the top-left level of the detection
The coordinates of the bottom-right level of the detection
The color of the rectangle (a tuple that defines the quantity of pink, inexperienced, and blue (0-255)).In our case, we set as inexperienced simply holding the inexperienced element as 255 and relaxation as zero.
The thickness of the rectangle strains

Subsequent, we simply show the ensuing body and in addition set a solution to exit this infinite loop and shut the video feed. By urgent the ‘q’ key, we will exit the script right here

 cv2.imshow('Video', body)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

The subsequent two strains are simply to scrub up and launch the image.

video_capture.launch()
cv2.destroyAllWindows()

Listed below are the complete code and output.

import cv2
import os
cascPath = os.path.dirname(
    cv2.__file__) + "/information/haarcascade_frontalface_alt2.xml"
faceCascade = cv2.CascadeClassifier(cascPath)
video_capture = cv2.VideoCapture(0)
whereas True:
    # Seize frame-by-frame
    ret, body = video_capture.learn()
    grey = cv2.cvtColor(body, cv2.COLOR_BGR2GRAY)
    faces = faceCascade.detectMultiScale(grey,
                                         scaleFactor=1.1,
                                         minNeighbors=5,
                                         minSize=(60, 60),
                                         flags=cv2.CASCADE_SCALE_IMAGE)
    for (x,y,w,h) in faces:
        cv2.rectangle(body, (x, y), (x + w, y + h),(0,255,0), 2)
        # Show the ensuing body
    cv2.imshow('Video', body)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
video_capture.launch()
cv2.destroyAllWindows()

Output:

Create a mannequin to acknowledge faces carrying a masks

On this part, we’re going to make a classifier that may differentiate between faces with masks and with out masks. In case you wish to skip this half, here’s a hyperlink to obtain the pre-trained mannequin. Reserve it and transfer on to the following part to know the right way to use it to detect masks utilizing OpenCV. Try our assortment of OpenCV programs that can assist you develop your expertise and perceive higher.

So for creating this classifier, we want information within the type of Photos. Fortunately now we have a dataset containing photographs faces with masks and and not using a masks. Since these photographs are very much less in quantity, we can’t prepare a neural community from scratch. As an alternative, we finetune a pre-trained community referred to as MobileNetV2 which is skilled on the Imagenet dataset.

Allow us to first import all the required libraries we’re going to want.

from tensorflow.keras.preprocessing.picture import ImageDataGenerator
from tensorflow.keras.purposes import MobileNetV2
from tensorflow.keras.layers import AveragePooling2D
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Enter
from tensorflow.keras.fashions import Mannequin
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.purposes.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.picture import img_to_array
from tensorflow.keras.preprocessing.picture import load_img
from tensorflow.keras.utils import to_categorical
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from imutils import paths
import matplotlib.pyplot as plt
import numpy as np
import os

The subsequent step is to learn all the pictures and assign them to some checklist. Right here we get all of the paths related to these photographs after which label them accordingly. Bear in mind our dataset is contained in two folders viz- with_masks and without_masks. So we will simply get the labels by extracting the folder identify from the trail. Additionally, we preprocess the picture and resize it to 224x 224 dimensions.

imagePaths = checklist(paths.list_images('/content material/drive/My Drive/dataset'))
information = []
labels = []
# loop over the picture paths
for imagePath in imagePaths:
	# extract the category label from the filename
	label = imagePath.break up(os.path.sep)[-2]
	# load the enter picture (224x224) and preprocess it
	picture = load_img(imagePath, target_size=(224, 224))
	picture = img_to_array(picture)
	picture = preprocess_input(picture)
	# replace the information and labels lists, respectively
	information.append(picture)
	labels.append(label)
# convert the information and labels to NumPy arrays
information = np.array(information, dtype="float32")
labels = np.array(labels)

The subsequent step is to load the pre-trained mannequin and customise it in accordance with our downside. So we simply take away the highest layers of this pre-trained mannequin and add few layers of our personal. As you’ll be able to see the final layer has two nodes as now we have solely two outputs. That is referred to as switch studying.

baseModel = MobileNetV2(weights="imagenet", include_top=False,
	input_shape=(224, 224, 3))
# assemble the pinnacle of the mannequin that shall be positioned on high of the
# the bottom mannequin
headModel = baseModel.output
headModel = AveragePooling2D(pool_size=(7, 7))(headModel)
headModel = Flatten(identify="flatten")(headModel)
headModel = Dense(128, activation="relu")(headModel)
headModel = Dropout(0.5)(headModel)
headModel = Dense(2, activation="softmax")(headModel)
# place the pinnacle FC mannequin on high of the bottom mannequin (this can grow to be
# the precise mannequin we are going to prepare)
mannequin = Mannequin(inputs=baseModel.enter, outputs=headModel)
# loop over all layers within the base mannequin and freeze them so they'll
# *not* be up to date throughout the first coaching course of
for layer in baseModel.layers:
	layer.trainable = False

Now we have to convert the labels into one-hot encoding. After that, we break up the information into coaching and testing units to judge them. Additionally, the following step is information augmentation which considerably will increase the range of knowledge out there for coaching fashions, with out truly gathering new information. Information augmentation strategies reminiscent of cropping, rotation, shearing and horizontal flipping are generally used to coach giant neural networks.

lb = LabelBinarizer()
labels = lb.fit_transform(labels)
labels = to_categorical(labels)
# partition the information into coaching and testing splits utilizing 80% of
# the information for coaching and the remaining 20% for testing
(trainX, testX, trainY, testY) = train_test_split(information, labels,
	test_size=0.20, stratify=labels, random_state=42)
# assemble the coaching picture generator for information augmentation
aug = ImageDataGenerator(
	rotation_range=20,
	zoom_range=0.15,
	width_shift_range=0.2,
	height_shift_range=0.2,
	shear_range=0.15,
	horizontal_flip=True,
	fill_mode="nearest")

The subsequent step is to compile the mannequin and prepare it on the augmented information.

INIT_LR = 1e-4
EPOCHS = 20
BS = 32
print("[INFO] compiling mannequin...")
decide = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
mannequin.compile(loss="binary_crossentropy", optimizer=decide,
	metrics=["accuracy"])
# prepare the pinnacle of the community
print("[INFO] coaching head...")
H = mannequin.match(
	aug.stream(trainX, trainY, batch_size=BS),
	steps_per_epoch=len(trainX) // BS,
	validation_data=(testX, testY),
	validation_steps=len(testX) // BS,
	epochs=EPOCHS)

Now that our mannequin is skilled, allow us to plot a graph to see its studying curve. Additionally, we save the mannequin for later use. Here’s a hyperlink to this skilled mannequin.

N = EPOCHS
plt.model.use("ggplot")
plt.determine()
plt.plot(np.arange(0, N), H.historical past["loss"], label="train_loss")
plt.plot(np.arange(0, N), H.historical past["val_loss"], label="val_loss")
plt.plot(np.arange(0, N), H.historical past["accuracy"], label="train_acc")
plt.plot(np.arange(0, N), H.historical past["val_accuracy"], label="val_acc")
plt.title("Coaching Loss and Accuracy")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend(loc="decrease left")

Output:

#To avoid wasting the skilled mannequin
mannequin.save('mask_recog_ver2.h5')

Find out how to do Actual-time Masks detection

Earlier than transferring to the following half, be sure that to obtain the above mannequin from this hyperlink and place it in the identical folder because the python script you’ll write the under code in.

Now that our mannequin is skilled, we will modify the code within the first part in order that it may detect faces and in addition inform us if the individual is carrying a masks or not.

To ensure that our masks detector mannequin to work, it wants photographs of faces. For this, we are going to detect the frames with faces utilizing the strategies as proven within the first part after which move them to our mannequin after preprocessing them. So allow us to first import all of the libraries we want.

import cv2
import os
from tensorflow.keras.preprocessing.picture import img_to_array
from tensorflow.keras.fashions import load_model
from tensorflow.keras.purposes.mobilenet_v2 import preprocess_input
import numpy as np

The primary few strains are precisely the identical as the primary part. The one factor that’s totally different is that now we have assigned our pre-trained masks detector mannequin to the variable mannequin.

ascPath = os.path.dirname(
    cv2.__file__) + "/information/haarcascade_frontalface_alt2.xml"
faceCascade = cv2.CascadeClassifier(cascPath)
mannequin = load_model("mask_recog1.h5")
video_capture = cv2.VideoCapture(0)
whereas True:
    # Seize frame-by-frame
    ret, body = video_capture.learn()
    grey = cv2.cvtColor(body, cv2.COLOR_BGR2GRAY)
    faces = faceCascade.detectMultiScale(grey,
                                         scaleFactor=1.1,
                                         minNeighbors=5,
                                         minSize=(60, 60),
                                         flags=cv2.CASCADE_SCALE_IMAGE)

Subsequent, we outline some lists. The faces_list accommodates all of the faces which are detected by the faceCascade mannequin and the preds checklist is used to retailer the predictions made by the masks detector mannequin.

faces_list=[]
preds=[]

Additionally for the reason that faces variable accommodates the top-left nook coordinates, top and width of the rectangle encompassing the faces, we will use that to get a body of the face after which preprocess that body in order that it may be fed into the mannequin for prediction. The preprocessing steps are identical which are adopted when coaching the mannequin within the second part. For instance, the mannequin is skilled on RGB photographs so we convert the picture into RGB right here

    for (x, y, w, h) in faces:
        face_frame = body[y:y+h,x:x+w]
        face_frame = cv2.cvtColor(face_frame, cv2.COLOR_BGR2RGB)
        face_frame = cv2.resize(face_frame, (224, 224))
        face_frame = img_to_array(face_frame)
        face_frame = np.expand_dims(face_frame, axis=0)
        face_frame =  preprocess_input(face_frame)
        faces_list.append(face_frame)
        if len(faces_list)>0:
            preds = mannequin.predict(faces_list)
        for pred in preds:
        #masks comprise probabily of carrying a masks and vice versa
            (masks, withoutMask) = pred

After getting the predictions, we draw a rectangle over the face and put a label in accordance with the predictions.

label = "Masks" if masks > withoutMask else "No Masks"
        coloration = (0, 255, 0) if label == "Masks" else (0, 0, 255)
        label = "{}: {:.2f}%".format(label, max(masks, withoutMask) * 100)
        cv2.putText(body, label, (x, y- 10),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.45, coloration, 2)
        cv2.rectangle(body, (x, y), (x + w, y + h),coloration, 2)

The remainder of the steps are the identical as the primary part.

cv2.imshow('Video', body)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
video_capture.launch()
cv2.destroyAllWindows()

Right here is the entire code and output:

import cv2
import os
from tensorflow.keras.preprocessing.picture import img_to_array
from tensorflow.keras.fashions import load_model
from tensorflow.keras.purposes.mobilenet_v2 import preprocess_input
import numpy as np
cascPath = os.path.dirname(
    cv2.__file__) + "/information/haarcascade_frontalface_alt2.xml"
faceCascade = cv2.CascadeClassifier(cascPath)
mannequin = load_model("mask_recog1.h5")
video_capture = cv2.VideoCapture(0)
whereas True:
    # Seize frame-by-frame
    ret, body = video_capture.learn()
    grey = cv2.cvtColor(body, cv2.COLOR_BGR2GRAY)
    faces = faceCascade.detectMultiScale(grey,
                                         scaleFactor=1.1,
                                         minNeighbors=5,
                                         minSize=(60, 60),
                                         flags=cv2.CASCADE_SCALE_IMAGE)
    faces_list=[]
    preds=[]
    for (x, y, w, h) in faces:
        face_frame = body[y:y+h,x:x+w]
        face_frame = cv2.cvtColor(face_frame, cv2.COLOR_BGR2RGB)
        face_frame = cv2.resize(face_frame, (224, 224))
        face_frame = img_to_array(face_frame)
        face_frame = np.expand_dims(face_frame, axis=0)
        face_frame =  preprocess_input(face_frame)
        faces_list.append(face_frame)
        if len(faces_list)>0:
            preds = mannequin.predict(faces_list)
        for pred in preds:
            (masks, withoutMask) = pred
        label = "Masks" if masks > withoutMask else "No Masks"
        coloration = (0, 255, 0) if label == "Masks" else (0, 0, 255)
        label = "{}: {:.2f}%".format(label, max(masks, withoutMask) * 100)
        cv2.putText(body, label, (x, y- 10),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.45, coloration, 2)
        cv2.rectangle(body, (x, y), (x + w, y + h),coloration, 2)
        # Show the ensuing body
    cv2.imshow('Video', body)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
video_capture.launch()
cv2.destroyAllWindows()

Output:

This brings us to the top of this text the place we realized the right way to detect faces in real-time and in addition designed a mannequin that may detect faces with masks. Utilizing this mannequin we had been in a position to modify the face detector to masks detector.

Replace: I skilled one other mannequin which may classify photographs into carrying a masks, not carrying a masks and never correctly carrying a masks. Here’s a hyperlink of the Kaggle pocket book of this mannequin. You may modify it and in addition obtain the mannequin from there and use it in as a substitute of the mannequin we skilled on this article. Though this mannequin is just not as environment friendly because the mannequin we skilled right here, it has an additional characteristic of detecting not correctly worn masks.

If you’re utilizing this mannequin it is advisable to make some minor modifications to the code. Exchange the earlier strains with these strains.

#Listed below are some minor modifications in opencv code
for (field, pred) in zip(locs, preds):
        # unpack the bounding field and predictions
        (startX, startY, endX, endY) = field
        (masks, withoutMask,notproper) = pred
        # decide the category label and coloration we'll use to attract
        # the bounding field and textual content
        if (masks > withoutMask and masks>notproper):
            label = "With out Masks"
        elif ( withoutMask > notproper and withoutMask > masks):
            label = "Masks"
        else:
            label = "Put on Masks Correctly"
        if label == "Masks":
            coloration = (0, 255, 0)
        elif label=="With out Masks":
            coloration = (0, 0, 255)
        else:
            coloration = (255, 140, 0)
        # embrace the likelihood within the label
        label = "{}: {:.2f}%".format(label,
                                     max(masks, withoutMask, notproper) * 100)
        # show the label and bounding field rectangle on the output
        # body
        cv2.putText(body, label, (startX, startY - 10),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.45, coloration, 2)
        cv2.rectangle(body, (startX, startY), (endX, endY), coloration, 2)

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Additional Studying

Actual-Time Object Detection Utilizing TensorFlow
YOLO object detection utilizing OpenCV
Object Detection in Pytorch | What’s Object Detection?

Face Recognition | Real Time Face Recognition OpenCV

What is Face Detection?

Face Detection Strategies

Function Base Strategy

Picture Base Strategy

Face detection algorithm