How do I crop faces with a neural network?
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I am looking to build a facial recognition system and realized I could probably pump up accuracy by first cropping the faces. I know I can use a Haar Cascade classifier to do this but would prefer to use a deep learning approach, as that is my current area of focus. To do this do I simply create a CNN with inputs being an image and outputs being the X, Y, Width, Height? Or is there more to it? I have tried this approach with an Adam optimizer and around 600 pictures but results haven't been great. Any ideas on how to improve this?
python deep-learning keras
asked 35 mins ago
user3647894user3647894
111
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I am looking to build a facial recognition system and realized I could probably pump up accuracy by first cropping the faces. I know I can use a Haar Cascade classifier to do this but would prefer to use a deep learning approach, as that is my current area of focus. To do this do I simply create a CNN with inputs being an image and outputs being the X, Y, Width, Height? Or is there more to it? I have tried this approach with an Adam optimizer and around 600 pictures but results haven't been great. Any ideas on how to improve this?
python deep-learning keras
asked 35 mins ago
user3647894user3647894
111
New contributor
user3647894 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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You may want to use pixel-wise classification, fully convolutional networks. YOLO based approaches can be considered too.
$endgroup$
– Media
25 mins ago
add a comment |
$begingroup$
I am looking to build a facial recognition system and realized I could probably pump up accuracy by first cropping the faces. I know I can use a Haar Cascade classifier to do this but would prefer to use a deep learning approach, as that is my current area of focus. To do this do I simply create a CNN with inputs being an image and outputs being the X, Y, Width, Height? Or is there more to it? I have tried this approach with an Adam optimizer and around 600 pictures but results haven't been great. Any ideas on how to improve this?
python deep-learning keras
asked 35 mins ago
user3647894user3647894
111
New contributor
user3647894 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am looking to build a facial recognition system and realized I could probably pump up accuracy by first cropping the faces. I know I can use a Haar Cascade classifier to do this but would prefer to use a deep learning approach, as that is my current area of focus. To do this do I simply create a CNN with inputs being an image and outputs being the X, Y, Width, Height? Or is there more to it? I have tried this approach with an Adam optimizer and around 600 pictures but results haven't been great. Any ideas on how to improve this?
python deep-learning keras
python deep-learning keras
asked 35 mins ago
user3647894user3647894
111
New contributor
user3647894 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 35 mins ago
user3647894user3647894
111
New contributor
user3647894 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 35 mins ago
user3647894user3647894
111
New contributor
user3647894 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 35 mins ago
user3647894user3647894
111
asked 35 mins ago
user3647894user3647894
111
111
New contributor
user3647894 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
user3647894 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
user3647894 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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You may want to use pixel-wise classification, fully convolutional networks. YOLO based approaches can be considered too.
$endgroup$
– Media
25 mins ago
add a comment |
$begingroup$
You may want to use pixel-wise classification, fully convolutional networks. YOLO based approaches can be considered too.
$endgroup$
– Media
25 mins ago
$begingroup$
You may want to use pixel-wise classification, fully convolutional networks. YOLO based approaches can be considered too.
$endgroup$
– Media
25 mins ago
$begingroup$
You may want to use pixel-wise classification, fully convolutional networks. YOLO based approaches can be considered too.
$endgroup$
– Media
25 mins ago
add a comment |
1 Answer
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You are looking for a fully convolution neural network trained for image segmentation. The architecture is typically convolutions followed by de-convolutional layers with out pooling.
The forward inference serves to extract information with convolutions. The de-convolutions serve to resize the tensors to the size of the inputs height and width. A fully connected layer then classifies each pixel from the input. Deconvolutions demonstration.
Unfortunately you need pixel wise labels to train a network to do this. You could look for a pretrained model that already segments faces from images or you could grind out hand labeling for a 1000 images and use transfer learning (there are online tools for doing this such as Label box.
answered 4 mins ago
J_HeadsJ_Heads
1473
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1 Answer
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1 Answer
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active
oldest
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active
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votes
$begingroup$
You are looking for a fully convolution neural network trained for image segmentation. The architecture is typically convolutions followed by de-convolutional layers with out pooling.
The forward inference serves to extract information with convolutions. The de-convolutions serve to resize the tensors to the size of the inputs height and width. A fully connected layer then classifies each pixel from the input. Deconvolutions demonstration.
Unfortunately you need pixel wise labels to train a network to do this. You could look for a pretrained model that already segments faces from images or you could grind out hand labeling for a 1000 images and use transfer learning (there are online tools for doing this such as Label box.
answered 4 mins ago
J_HeadsJ_Heads
1473
$endgroup$
add a comment |
$begingroup$
You are looking for a fully convolution neural network trained for image segmentation. The architecture is typically convolutions followed by de-convolutional layers with out pooling.
The forward inference serves to extract information with convolutions. The de-convolutions serve to resize the tensors to the size of the inputs height and width. A fully connected layer then classifies each pixel from the input. Deconvolutions demonstration.
Unfortunately you need pixel wise labels to train a network to do this. You could look for a pretrained model that already segments faces from images or you could grind out hand labeling for a 1000 images and use transfer learning (there are online tools for doing this such as Label box.
answered 4 mins ago
J_HeadsJ_Heads
1473
$endgroup$
add a comment |
$begingroup$
You are looking for a fully convolution neural network trained for image segmentation. The architecture is typically convolutions followed by de-convolutional layers with out pooling.
The forward inference serves to extract information with convolutions. The de-convolutions serve to resize the tensors to the size of the inputs height and width. A fully connected layer then classifies each pixel from the input. Deconvolutions demonstration.
Unfortunately you need pixel wise labels to train a network to do this. You could look for a pretrained model that already segments faces from images or you could grind out hand labeling for a 1000 images and use transfer learning (there are online tools for doing this such as Label box.
answered 4 mins ago
J_HeadsJ_Heads
1473
$endgroup$
You are looking for a fully convolution neural network trained for image segmentation. The architecture is typically convolutions followed by de-convolutional layers with out pooling.
The forward inference serves to extract information with convolutions. The de-convolutions serve to resize the tensors to the size of the inputs height and width. A fully connected layer then classifies each pixel from the input. Deconvolutions demonstration.
Unfortunately you need pixel wise labels to train a network to do this. You could look for a pretrained model that already segments faces from images or you could grind out hand labeling for a 1000 images and use transfer learning (there are online tools for doing this such as Label box.
answered 4 mins ago
J_HeadsJ_Heads
1473
answered 4 mins ago
J_HeadsJ_Heads
1473
answered 4 mins ago
J_HeadsJ_Heads
1473
answered 4 mins ago
J_HeadsJ_Heads
1473
1473
add a comment |
add a comment |
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$begingroup$
You may want to use pixel-wise classification, fully convolutional networks. YOLO based approaches can be considered too.
$endgroup$
– Media
25 mins ago