Daisy Chaining Machine Learning Models for End-to-End Facial Recognition

In this article, we will explore how an engineer can leverage a series of pre-trained machine-learning models to create a comprehensive facial recognition system. This system will be capable of identifying faces in an image, detecting text within the image, converting that image to text, and finally translating the text. Each component of this process is driven by a different machine-learning model. The use of multiple models in this fashion is often referred to as 'daisy-chaining' models.

Outline of the Solution

The overall system can be divided into four primary steps, each involving a different pre-trained machine-learning model:

  1. Face detection using a pre-trained model like MTCNN (Multi-task Cascaded Convolutional Networks)
  2. Text detection in an image using an algorithm like EAST (Efficient and Accurate Scene Text Detector)
  3. Text recognition from the detected text areas using an OCR (Optical Character Recognition) tool like Tesseract
  4. Text translation using a pre-trained NMT (Neural Machine Translation) model

Let's dive into the details of each step.

1. Face Detection with MTCNN

The MTCNN is a popular model used for face detection due to its high accuracy. Below is a simple Python code snippet demonstrating its usage.

from mtcnn import MTCNN
from PIL import Image

# Initialize the detector
detector = MTCNN()

# Open an image file
image = Image.open("image.jpg")
image = image.convert('RGB')

# Detect faces in the image
faces = detector.detect_faces(pixels=np.asarray(image))

# Print the bounding box for each detected face
for face in faces:
    print(face['box'])

2. Text Detection with EAST

The EAST algorithm is widely used for text detection in images because it can detect text regardless of its orientation. Here's a Python code snippet showing how to use EAST:

import cv2
import numpy as np

# Load the pre-trained EAST model
net = cv2.dnn.readNet("frozen_east_text_detection.pb")

# Load the image
image = cv2.imread("image.jpg")

# Preprocess the image for text detection
blob = cv2.dnn.blobFromImage(image, 1.0, (320, 320), (123.68, 116.78, 103.94), True, False)

# Set blob as input to the network
net.setInput(blob)

# Perform a forward pass to compute output feature maps of two layers
(scores, geometry) = net.forward(["feature_fusion/Conv_7/Sigmoid", "feature_fusion/concat_3"])

# The result will be bounding boxes and confidence scores for text detection

3. Text Recognition with Tesseract OCR

Once we have the bounding boxes for text, we can extract the regions and convert them to actual text strings using Tesseract.

import pytesseract
from pytesseract import Output

# Load the image
image = cv2.imread('image.jpg')

# For each bounding box detected by EAST
for (startX, startY, endX, endY) in boxes:
    # Extract the actual padded ROI
    roi = image[startY:endY, startX:endX]

    # Use Tesseract to convert the image into text
    text = pytesseract.image_to_string(roi, config=config)

    # Print the text
    print(text)

4. Text Translation with Neural Machine Translation (NMT)

The final stage of our pipeline involves translating the extracted text into the desired language. Here, we'll use the Hugging Face Transformers library, which provides pre-trained NMT models.

from transformers import MarianMTModel, MarianTokenizer

# Specify the model
model_name = 'Helsinki-NLP/opus-mt-en-fr' # English to French

# Load pre-trained model and tokenizer
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)

# For each text string identified by Tesseract
for text in texts:
    # Tokenize the text
    tokenized_text = tokenizer(text, return_tensors='pt')

    # Generate translation
    translated = model.generate(**tokenized_text)

    # Decode the translation
    translation = tokenizer.decode(translated[0], skip_special_tokens=True)

    # Print the translation
    print(translation)

Now tune, version, and scale...

Luckily we're building ML tooling to assist with not only daisy-chaining the outputs of models into the inputs of others, but we're also abstracting the hosting, versioning, tuning, and inference to a couple of lines of code.

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