Facial recognition neural network









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Face Recognition Using Artificial Neural Networks

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The neural network for face recognition

For every bounding box, we create an array of the same size, and copy the pixel values the image in the bounding box to the new array. However, there are still a lot of bounding boxes left, and a lot of them overlap. We conduct NMS once for every scaled image, then one more time with all the surviving kernels from each scale. However, because the stride is 2, we have to multiply the index by 2 to get the correct coordinate. In addition, this model employs an image pyramid to find faces both large and small. O-Net provides 3 outputs. The weights and biases of P-Net have been trained so that it outputs a relatively accurate bounding box for every 12 x 12 kernel. At the same time, your computer or whatever machine is running this code will have a quarter of the amount of operations to compute, making the program run faster and with less memory. By multiplying the coordinates by the actual image width and height, we can convert the bounding box coordinates to the standard, real-sized image coordinates. High accuracy is achieved with a deep neural network. Finally, we run them through the last NMS.

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Description: This is the function that you would call when implementing this model, so going through this function would give you a sense of how the program calculates and narrows down the coordinates of the bounding box and facial features. Package all coordinates and confidence levels into a dictionary Return the dictionary. Can it be adjusted to do facial recognition as well as facial detection. If the bounding box is out of bounds, we only copy the portion of the image in the bounding box to the new array and fill in everything else with a 0. Having a stride of 2 helps reduce computation complexity without significantly sacrificing accuracy. Sometimes, an image may contain only a part of a face peeking in from the side of the frame. We can calculate the width and height of the kernel. By multiplying the coordinates by the actual image width and height, we can convert the bounding box coordinates to the standard, real-sized image coordinates. The very last step is to package all the information into a dictionary with three keys. Right now, the coordinates of each bounding box is a value between 0 and 1, with 0,0 as the top left corner of the 12 x 12 kernel and 1,1 as the bottom right corner see table above.
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