Face detection

Image Processing in Python

Rebeca Gonzalez

Data Engineer

Face detection use cases

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To recognize emotions later on

Detecting faces with scikit-image

# Import the classifier class
from skimage.feature import Cascade


# Load the trained file from the module root.
trained_file = data.lbp_frontal_face_cascade_filename()


# Initialize the detector cascade.
detector = Cascade(trained_file)

Let's try it

Detecting faces

# Apply detector on the image
detected = detector.detect_multi_scale(img=image,

                                        scale_factor=1.2,

                                        step_ratio=1,

                                        min_size=(10, 10),
                                        max_size=(200, 200))

Detected faces

print(detected)

# Show image with detected face marked
show_detected_face(image, detected)

Detected face: [{'r': 115, 'c': 210, 'width': 167, 'height': 167}]

Show detected faces

def show_detected_face(result, detected, title="Face image"):
    plt.imshow(result)
    img_desc = plt.gca()
    plt.set_cmap('gray')
    plt.title(title)
    plt.axis('off')

    for patch in detected:
        img_desc.add_patch(
            patches.Rectangle(
                (patch['c'], patch['r']),
                patch['width'],
                patch['height'],
                fill=False,color='r',linewidth=2)
        )
    plt.show()

Let's practice!

Image Processing in Python