Face recognition in OpenCv, Tensorflow-keras with Dlib face detector and Vgg face model. python cnn-face-detector-dlib.py -i input.jpg (This will work if both the input.jpg and model weights file are in the current directory same as the python script) Or you can run by typing, python cnn-face-detector-dlib.py -i -w The model gets 128 embeddings from the image. The face_recognition library is widely known around the web for being the world's simplest facial recognition api for Python and the command line, and the best of all is that you won't need to pay a dime for it, the project is totally open source, so if you have some development knowledge and you are able to build a library from scratch, you'll surely know how to work with this library. Davis E. King modified the regular ResNet structure and dropped some layers and re-build a neural networks consisting of 29 convolution layers. See LICENSE. These models were created by Davis King and are licensed in the public domain or under CC0 1.0 Universal. Face Recognition Models. Just like all the other example dlib models, the pretrained model used by this example program is in the public domain.So you can use it for anything you want. I am using a face recognition library to detect faces. 3. Dlib is mainly inspired from a ResNet-34 model. Isolate each face in the image and align the face using dlib such that every face has its OUTER EYES and NOSE present at the same position. This package contains only the models used by face_recognition.. See face_recognition for more information.. Generate the embedding.

In my experience using both OpenCV’s face recognition model along with dlib’s face recognition model, I’ve found that dlib’s face embeddings are more discriminative, especially for smaller datasets. He then re-trained the model for various data sets including FaceScrub and VGGFace2. The second reason is that dlib is unable to detect small faces which further drags down the numbers. Also, the model has an accuracy of 99.38% on the standard Labeled Faces in the Wild benchmark. Model. This only means that the Dlib models are able to detect more faces than that of Haar, but the smaller bounding boxes of dlib lower their AP_75 and other numbers.

ResNet-34. It expexts 150x150x3 sized inputs and represent face images as 128 dimensional vectors.