With the introduction of computers and machines, their capability to perform various tasks went on growing exponentially humans interactively used this power of machines in various working domains to increase the speed and reduce the size with respect to time.
Artificial Intelligence is the branch of computer science which purses the art of creating Intelligent machines as human beings. It is nothing but controlling and performing the tasks with the help of predefined computer programs. Its goal is to create expert systems which deliver accurate functions. It is the way of implementing or using human intelligence in machines.
AI is nothing but the power given to computers so as to think intelligently as that of humans.
AI concept had been accomplished by studying how the human brain thinks, how humans learn, decide and work while trying to solve a problem.
The basic philosophy of AI is to exploit machine intelligence as that of humans. This theory of AI leads to getting success by implementing human intelligence in machines.
AI Mainly contributes to science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics, and engineering.
The major trust of AI is in the development of computer functions associated with human intelligence, such as reasoning, learning and problem-solving.
Coming to the real fact the knowledge has unwelcomed properties such huge volume which is informal and changes constantly so here AI technique is used to organize and use knowledge efficiently. One can do easy modifications and is used in many situations to elevate the speed of execution.
The IBM AI Watson delivers accurate measures and implements them in a strategic manner. IBM AI delivers its services in all the fields such as Education, finance, government, face, voice, speech recognition techniques, gaming, natural processing systems and in creating intelligent robots. IBM AI services include a very good approach to create faster applications with coherent results. It captures the data and drives insights in real time.
The IBM Watson AI has the power to unlock the deployment for databases and deep learning frameworks with the help of right processor architecture and server hardware. IBM uses the fast and flexible platforms for the AI as it helps the developers to build applications which take to great heights. The developers use the very effective tools which scale up their performances. In order to crunch large volume of data AI requires, it uses the massive processing power, high throughput, and GPU acceleration.
IBM comes up with the best software in the fields of computation and storage so as to provide optimized AI workloads across the environment. With the help of IBM AI services, you can get intelligent answers to most of the challenging questions. With the traditional processing data requires human power and lot of time goes waste. So to make and use your available time wisely IBM AI services helps a lot. Because AI services help to grab the data analytics in deep without a single point of failure. You can get the best results which are highly unique and optimized too.
5 Steps to ensure successful AI Application deployment.
- Make a plan:
As a developer of an application or module, you need to gather the information regarding so as to establish a proper connection with your sample data. You need to make the things done one after the other. Your strategy to implement the AI application or module should be very focused and clear. So that you carry on with the available software, hardware or any other operating systems etc.
- Collect the data required for processing:
You need to collect the data or make a ready so as to quick start your project. You need to specifically gather the requirements needed and put them all together. You need to check whether they are connected or interlinked so as to drive good analytics.
- You need to define the data structure:
Most probably there might be several logics to get the estimated results but all not equal. If you want to drive unique and estimate results you need to specify and define the elements properly.
- You need to establish data governance:
After defining the data structure, it’s very important to establish the connection between the available elements. By establishing the data connection you can signify the results for appropriate. Data governance resembles the source or given input as trustworthy.
- Make a safe and secure discipline to execute the application:
After making a strategy plan, defining the data and establish a connection you need to use the safety measure where the IBM AI services provide a strong support in this aspect.
With the IBM AI Services, one can drive qualitative and outstanding responses.
To be more practical many IBM AI services help many developers to build useful applications which are reliable and trustworthy. They had widespread their services in many fields form telecom to the Movie industry and in creating artificial robots, healthcare to diagnose many issues. as well.
Now coming to the point let me discuss the automatic face recognition system and how IBM AI services help to detect and find the neutral parts of the face such as eyes, nose, mouth, lips and even any single spot on your face can be recognized.
How do IBM AI services help to detect automatic face recognition?
It’s very quite interesting for the developers to create everlasting AI solutions for their business.
With the help of Viola-Jones Algorithm, one can design and create the system to recognize the face, eyes and each and every space on your face. This algorithm helps not only to detect faces on images but also on videos as well.
In order to detect the neutral parts of faces, you need to pick up a photo which you need to spot out based on some actions. The actions might be looking for the faces with the rectangle boxes from the top right corner of the photo. It might be looking for the eyes, nose, lips, eyebrows, cheeks and certain features as well. It detects the faces with the pixels in a box. In order to detect a face the box should contain the two eyebrows, two eyes, nose and mouth then it can be recognized as the face. Until the face is detected with those features it keeps on going looking to figure out a structure.
Watson IBM services help the developers to find or recognize the neutral faces, eyes by highlighting the various spots on the face. You can make the systems recognize by developing the simple code, by creating the objects for the parts to be identified and writing functions based on the action to be performed. With the Haar-like features of the Viola-Jones Algorithm, one can spot the facial features. The integral image helps to spot out the specific elements of the face by calculating the values.
First of in order to detect the faces and files you need to create two XML files for the eyes and frontal face. In those cascade files, you need to specify your requirements and dimensions such as height, width, and x, y coordinates for the image respectively. With the help of those coordinates given by you, helps to detect the frontal face parts.
<?xml version="1.0"?> <!-- <opencv_storage> <cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType> <featureType>HAAR</featureType> <height>24</height> # Needs to specify the heights and width of the frontal face <width>24</width> <stageParams> <maxWeakCount>211</maxWeakCount></stageParams> <featureParams> <maxCatCount>0</maxCatCount></featureParams> <stageNum>25</stageNum> <stages> <_> <maxWeakCount>9</maxWeakCount> <stageThreshold>-5.0425500869750977e+00</stageThreshold> <weakClassifiers> <_> <internalNodes> 0 -1 0 -3.1511999666690826e-02</internalNodes> <leafValues> 2.0875380039215088e+00 -2.2172100543975830e+00</leafValues></_> <_> </opencv_storage>
The logic continues until you find a perfect match for detecting the face.
<?xml version="1.0"?> <!-- <opencv_storage> <cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType> <featureType>HAAR</featureType> <height>20</height> <width>20</width> <stageParams> <maxWeakCount>93</maxWeakCount></stageParams> <featureParams> <maxCatCount>0</maxCatCount></featureParams> <stageNum>24</stageNum> <stages> <_> <maxWeakCount>6</maxWeakCount> <stageThreshold>-1.4562760591506958e+00</stageThreshold> <weakClassifiers> <_> # It is for checking the eyes based on some specific values. <internalNodes> 0 -1 0 1.2963959574699402e-01</internalNodes> <leafValues> -7.7304208278656006e-01 6.8350148200988770e-01</leafValues></_> </opencv_storage>
The above-mentioned cascade XML file helps to detect the eyes based on the coordinates and rectangle boxes.
When you are ready with all the cascading elements required to carry out the functionality i.e automatic face detecting you need to install all the software, hardware, libraries required to deploy your project.
Code for Automatic Face Recognition Systems:
Let us walk the code how IBM AI uses to detect neutral faces or different facial expressions as well.
As the first part of the code you need to create the cascades for the elements to be called and then you can call those cascades by writing the action code so as to automatic detection of faces. You need to import all the cascading files into your virtual environment where you are going to execute the code.
First, you need to import the libraries required for the function to be executed.
Step1: # Importing the libraries
Step2: After importing the libraries you need to load the cascading files one for eye and one for a face which you had created.
# Loading the cascades for face and eyes.
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
Step3: # Defining a function that will do the detections for face and eyes.
def detect(gray, frame) #We create a function that takes as input the image in black and white (gray) and the original image (frame), and that will return the same image with the detector rectangles
faces = face_cascade.detectMultiScale(gray, 1.3, 5) # We apply the detectMultiScale method from the face cascade to locate one or several faces in the image.
for (x, y, w, h) in faces: #For loop repetition to detect the faces.
#we can get a region of the rectangle with the black and white or even colored image with the below-mentioned code.
cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2) roi_gray = gray[y:y+h, x:x+w] roi_color = frame[y:y+h, x:x+w]
# We apply the detectMultiScale method to locate one or several eyes in the image. For each detected eye. We paint a rectangle around the eyes but inside the referential of the face.
eyes = eye_cascade.detectMultiScale(roi_gray, 1.1, 3) for (ex, ey, ew, eh) in eyes: cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2)
return frame # We return the image with the detector rectangles.
Step4: # Doing some Face Recognition with the webcam
video_capture = cv2.VideoCapture(0) # You need to turn on the webcam on
_, frame = video_capture.read()
#With the help of while loop it repeats infinitely until breakpoint occurs. We get the last frame with the frame logic.
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # We do some colour transformations. canvas = detect(gray, frame) # We get the output of our detect function. cv2.imshow('Video', canvas) # We display the outputs. if cv2.waitKey(1) & 0xFF == ord('q'): # If we type on the keyboard: break # We stop the loop.
Step5: # We turn the webcam off. We destroy all the windows inside which the images were displayed with the below code.
The above described cascading XML files and detection code helps any developer to make unique solutions for the application.
In a more perspective and an easy way, one can deliver enormous results by using IBM AI services. It helps the developers to create realistic applications with high versatile speed. The software, frameworks which the IBM AI uses helps any developer to get fast access and insights. The AI Watson services help everyone especially developers to come up with target based AI solutions.