The scope of this homework is to evaluate qualitatively and quantitatively a face detection method. What you should do: 1. Find (download or capture) 10 images, each containing at least 3 faces "in the wild" (i.e., in unconstrained environments, e.g., outdoors and/or in cluttered surroundings) 2. Use either Viola-Jones method (see HW1) or a Convolutional NN-based method to detect the faces in the 10 images. (Obviously, the ground-truth faces should be more than 30.) 3. Repeat #2 9 more times, increasingly smoothing/blurring the images. 4. Report the confusion matrix = {True Positives, True Negatives, False Positives, False Negatives} for each of the 10 repetitions, with respect to the detections. (If a face is detected, it does not matter how much of the face is left outside the detection or how much of the background is included in the detection.) 5. Plot the corresponding Receiver-Operating Characteristic (ROC) curve.

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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In Python and I need a report describing the work as well. Thank you.

The
scope of this homework is to evaluate qualitatively and quantitatively a face detection method. What you should do:
1. Find (download or capture) 10 images, each containing at least 3 faces "in the wild" (i.e., in unconstrained environments, e.g., outdoors and/or in cluttered
surroundings)
2. Use either Viola-Jones method (see HW1) or a Convolutional NN-based method to detect the faces in the 10 images. (Obviously, the ground-truth faces
should be more than 30.)
3. Repeat #2 9 more times, increasingly smoothing/blurring the images.
4. Report the confusion matrix = {True Positives, True Negatives, False Positives, False Negatives} for each of the 10 repetitions, with respect to the
detections. (If a face is detected, it does not matter how much of the face is left outside the detection or how much of the background is included in the
detection.)
5. Plot the corresponding Receiver-Operating Characteristic (ROC) curve.
Transcribed Image Text:The scope of this homework is to evaluate qualitatively and quantitatively a face detection method. What you should do: 1. Find (download or capture) 10 images, each containing at least 3 faces "in the wild" (i.e., in unconstrained environments, e.g., outdoors and/or in cluttered surroundings) 2. Use either Viola-Jones method (see HW1) or a Convolutional NN-based method to detect the faces in the 10 images. (Obviously, the ground-truth faces should be more than 30.) 3. Repeat #2 9 more times, increasingly smoothing/blurring the images. 4. Report the confusion matrix = {True Positives, True Negatives, False Positives, False Negatives} for each of the 10 repetitions, with respect to the detections. (If a face is detected, it does not matter how much of the face is left outside the detection or how much of the background is included in the detection.) 5. Plot the corresponding Receiver-Operating Characteristic (ROC) curve.
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