Facial | Simple interface to OpenCV object detection | Computer Vision library

So I tried the methods that were mentioned in the previously asked similar question but none of them works for my python file. I have been on it for two days and can't seem to find a solution how to run this file from C# form on button click.

IronPython doesn't work because the python script has libraries that cannot be imported in Ironpython.

Running it from cmd doesn't work because cmd starts and then gets closed in a second.

Here's the code:

I have a pytorch model I'm trying to use to do facial recognition. I am using the same model structure, loss, and optimizer as a working code, but it seems like the backprop won't do anything, any output of the NN is just 0.5. Here is the code, any help/suggestions is/are appreciated.

What is [0] and (1) in: r = np.random.randint((1), 24000, 1)[0]

Entire github code:

https://github.com/Pawandeep-prog/facial-emotion-detection-webapp/blob/main/facial-detection.py#L230

I want to transform the output of Google Vision API facial recognition into a feature set for a ML classifier. For each training instance I get a list of predicted faces which is represented as a list of dictionaries where the values are themselves dictionaries and the values of these 'value dictionaries' are categorical in nature like this:

I am building an android app which will authenticate user with AWS Rekognition facial verification. The app might be running in remote areas where internet and cellular connectivity are not available.

It it possible to pre-download all the face metadata stored in AWS and perform facial verification offline in the android app?

This is rather a theoretical question than asking for specific code issue.

I have done a bit of facial landmark detection using Haar Cascades, but this time I have a different type of video on my hands. It's a side view of a horse's eye (camera is mounted to the side of the head) so essentially what I see is a giant eye. I tried using Haar Cascades but it's no use, since there is no face to be detected in my video.

I was wondering what the best way to detect the eye and blinks would be on this horse? Do I try and customize a dlib facial mark detector? I didn't find much information on animal landmarks.

Thanks in advance! :)

Are 68 landmark points used in dlib https://towardsdatascience.com/facial-mapping-landmarks-with-dlib-python-160abcf7d672 a subset of 106 landmark points used in JD challenge https://facial-landmarks-localization-challenge.github.io/? If it is a subset what are the indices for conversion?

I've created a facial recognition program, and I was wondering which would be the best way to input the current time into the sqlite database. I've currently managed to update the attendance of the user, but I'm not quite sure on how to update the time of detection. Below is some of the code I've done for the system.

I'm trying to implement a 3D facial recognition algorithm using CNNs with multiple classes. I have an image generator for rgb images, and an image generator for depth images (grayscale). As I have two distinct inputs, I made two different CNN models, one with shape=(height, width, 3) and another with shape=(height, width, 1). Independently I can fit the models with its respective image generator, but after concatenating the two branches and merging both image generators, I got this warning and error:

WARNING:tensorflow:Model was constructed with shape (None, 400, 400, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 400, 400, 1), dtype=tf.float32, name='Depth_Input_input'), name='Depth_Input_input', description="created by layer 'Depth_Input_input'"), but it was called on an input with incompatible shape (None, None)

"ValueError: Input 0 of layer Depth_Input is incompatible with the layer: : expected min_ndim=4, found ndim=2. Full shape received: (None, None)"

What can i do to solve this? Thanks

Here is my code: