Facial recognition neural network
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The only downside is that we have to recalculate all indexes related to the stride. Sometimes, an image may contain only a part of a face peeking in from the side of the frame. Since the bounding boxes may not be square, we then reshape the bounding boxes to a square by elongating the shorter sides if the width is smaller than the height, we expand it sideways. if the height is smaller than the width, we expand it vertically. 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. 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.
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Artificial Neural Networks For Face Recognition Using Pca
There is only one face in the image above. High accuracy is achieved with a deep neural network. Each kernel would be smaller relative to a large image, so it would be able to find smaller faces in the larger-scaled image. 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.
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The neural network for face recognition
Sometimes, an image may contain only a part of a face peeking in from the side of the frame. The kernels that overlap a lot with the high-scoring kernel get deleted. There is only one face in the image above. For each scaled copy, we have a 12 x 12 stage 1 kernel that will go through every part of the image, scanning for faces.
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