update readme

README.md

@@ -2,9 +2,63 @@
 
 **face recognition API for the browser with tensorflow.js**
 
-coming soon ...
+This project implements a ResNet-34 like architecture using the tensorflow.js core API ([@tensorflow/tfjs-core](https://github.com/tensorflow/tfjs-core)) for realtime face recognition in the browser. The neural net is equivalent to the *FaceRecognizerNet* used in [face-recognition.js](https://github.com/justadudewhohacks/face-recognition.js) and the net used in the [dlib](https://github.com/davisking/dlib/blob/master/examples/dnn_face_recognition_ex.cpp) face recognition example. The weights have been trained by [davisking](https://github.com/davisking) and the model achieves a prediction accuracy of 99.38% on the LFW (Labeled Faces in the Wild) benchmark for face recognition.
 
-![preview](https://user-images.githubusercontent.com/31125521/40273537-f4e88e82-5bc1-11e8-9472-a5f06087dfd7.gif)
+## Face Recognition
+
+![preview_face-recognition](https://user-images.githubusercontent.com/31125521/40313021-c3afdfec-5d14-11e8-86df-cf89a00668e2.gif)
+
+## Face Similarity
+
+![preview_face-similarity](https://user-images.githubusercontent.com/31125521/40313983-6bade070-5d17-11e8-894c-6b4b52a2d61d.gif)
+
+## Usage
+
+Get the latest build from dist/face-recognition.min.js and include the script:
+
+``` html
+<script src="face-recognition.min.js"></script>
+```
+
+Download the weights file from your server and initialize the net (note, that your server has to host the *face_recognition_model.weights* file):
+
+``` javascript
+const res = await axios.get('face_recognition_model.weights', { responseType: 'arraybuffer' })
+const weights = new Float32Array(res.data)
+const net = facerecognition.faceRecognitionNet(weights)
+```
+
+Compute and compare two 150 x 150 sized face images:
+
+``` javascript
+// input can be an ImageData object obtained from a 150 x 150 canvas via ctx.getImageData(0, 0, 150, 150)
+// or a flat array with length 3*150*150 with pixel values in rgb order
+// also have a look at the examples how to get the image data from a base64 string, from an <img>, from a <canvas> ...
+const imgData1 = ...
+const imgData2 = ...
+
+
+const descriptor1 = await net.computeFaceDescriptor(imgData1)
+const descriptor2 = await net.computeFaceDescriptor(imgData2)
+const distance = facerecognition.computeFaceDescriptor(descriptor1, descriptor2)
+
+if (distance < 0.6)
+  console.log('match')
+else
+  console.log('no match')
+```
+
+You can also get the face descriptor data synchronously:
+
+``` javascript
+const desc = net.computeFaceDescriptorSync(imgData1)
+```
+
+Or simply obtain the tensor:
+
+``` javascript
+const t = net.forward(imgData1)
+```
 
 ## Running the examples