Creating a Facial Recognition App Using Python, Tensorflow, and Kivy

In this tutorial, we will build a Python facial recognition app using Tensorflow and Kivy. Tensorflow is an open-source machine learning framework developed by Google, while Kivy is a Python framework for developing multitouch applications. By combining these two powerful tools, we can create a facial recognition app that can identify faces in real-time.

Before we begin, make sure you have Tensorflow and Kivy installed on your system. You can install Tensorflow using pip:

pip install tensorflow

You can install Kivy using pip as well:

pip install kivy

Once you have both Tensorflow and Kivy installed, we can start building our facial recognition app. We will be using a pre-trained model called OpenCV for face detection. OpenCV is a popular computer vision library that provides pre-trained models for detecting faces, objects, and more.

First, let’s create a new Python file called facial_recognition.py and import the necessary libraries:

import cv2
import numpy as np
from kivy.app import App
from kivy.uix.boxlayout import BoxLayout
from kivy.uix.image import Image
from kivy.clock import Clock

Next, let’s define our FacialRecognition class that will inherit from the App class:

class FacialRecognition(App):
    def build(self):
        self.layout = BoxLayout()
        self.image = Image()
        self.layout.add_widget(self.image)
        Clock.schedule_interval(self.detect_faces, 1.0 / 30.0)
        return self.layout

In the FacialRecognition class, we have a build method that creates a BoxLayout and an Image widget. We add the Image widget to the layout, and we use Clock.schedule_interval to call the detect_faces method every 1/30 of a second.

Next, let’s define the detect_faces method that will use the OpenCV face detection model to detect faces in real-time:

def detect_faces(self, dt):
    cap = cv2.VideoCapture(0)
    ret, frame = cap.read()
    cap.release()

    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)

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

    buf = cv2.flip(frame, 0).tostring()
    texture = self.image.texture
    texture.blit_buffer(buf, colorfmt='bgr', bufferfmt='ubyte')
    self.image.texture = texture

In the detect_faces method, we capture a frame from the default camera using cv2.VideoCapture(0). We then convert the frame to grayscale and use the OpenCV face detection model to detect faces in the frame. We draw rectangles around the detected faces and update the Image widget with the processed frame.

Now, we can run our facial recognition app by creating an instance of the FacialRecognition class and calling the run method:

if __name__ == '__main__':
    FacialRecognition().run()

That’s it! You have just built a Python facial recognition app using Tensorflow and Kivy. You can further enhance the app by training your own facial recognition model using Tensorflow or adding more advanced features such as facial recognition authentication.

I hope you found this tutorial helpful. If you have any questions or run into any issues, feel free to ask in the comments below. Happy coding!