Building a "Minimum Viable Product" (MVP) with Face recognition and AR in Android @ Droidcon London 2017

Raúl Hernández López Software Engineer focused on Android Building a
“Minimum Viable Product” (MVP) with Face Recognition and AR in Android @RaulHernandezL droidcon London 2017 raulh82vlc raul.h82 #droidconUK

A User Story @RaulHernandezL #MVP #droidconUK

How? @RaulHernandezL #MVP #droidconUK

First things first: Proof of concept @RaulHernandezL #MVP #droidconUK

@RaulHernandezL #MVP #droidconUK Face detection Proof of concept

@RaulHernandezL #MVP #droidconUK Face detection Augmented Reality Proof of concept

@RaulHernandezL #MVP #droidconUK Face recognition Proof of concept

@RaulHernandezL #MVP #droidconUK Brainstorm

@RaulHernandezL A proper hat with a bow tie Brainstorm #MVP
#droidconUK

@RaulHernandezL Brainstorm ...also a smoking pipe and why not a
smart moustache and a monocle to impress!!! #MVP #droidconUK

Use case @RaulHernandezL #MVP #droidconUK Is this what we want???

Use case @RaulHernandezL Definitely too complex Stop it! #MVP #droidconUK

What do we really need to achieve? @RaulHernandezL #MVP #droidconUK

What are the goals? @RaulHernandezL #MVP #droidconUK Build a Prototype

What are the goals? @RaulHernandezL #MVP #droidconUK Build a Prototype
Something functional

Build a Prototype Something functional What are the goals? @RaulHernandezL
(but simple!) #MVP #droidconUK

What are the goals? @RaulHernandezL Build a Prototype Something functional
Easy to be extended #MVP #droidconUK

And remember @RaulHernandezL #MVP #droidconUK simple!

Use case @RaulHernandezL #AR #MachineLearning #droidconUK Kind of...

High level Requirements @RaulHernandezL Selfie camera #AR #MachineLearning #droidconUK

High level Requirements @RaulHernandezL #AR #MachineLearning #droidconUK Face detection with
Points of interest (POIs) to show AR

High level Requirements @RaulHernandezL #AR #MachineLearning #droidconUK Face recognition Raul

How do I start now? @RaulHernandezL #AR #MachineLearning #droidconUK

How do I start to build my detection system? @RaulHernandezL
#AR #MachineLearning #droidconUK Choose a machine learning library:

OpenCV workflow @RaulHernandezL #AR #MachineLearning #droidconUK https://github.com/raulh82vlc/Image-Detection-Samples

Face detection @RaulHernandezL Detection model (cascade classifier) Haar Cascade vs
Local Binary Pattern Histograms (LBPH) #AR #MachineLearning #droidconUK https://github.com/raulh82vlc/Image-Detection-Samples

@RaulHernandezL #AR #MachineLearning #droidconUK @NonNull private MatOfRect startDetection() { if
(absoluteFaceSize == 0) { int height = matrixGray.rows(); if (Math.round(height * RELATIVE_FACE_SIZE) > 0) { absoluteFaceSize = Math.round(height * RELATIVE_FACE_SIZE); } } MatOfRect faces = new MatOfRect(); if (detectorFace != null) { if (matrixGray.height() > 0) { detectorFace.detectMultiScale(matrixGray, faces, 1.1, 2, 2, new Size(absoluteFaceSize, absoluteFaceSize), new Size()); } } return faces; } Image in gray output opencv.domain.FDInteractorImpl Haar Cascade classifier OpenCV face detection

@RaulHernandezL #AR #MachineLearning #droidconUK @NonNull private MatOfRect startDetection() { if
(absoluteFaceSize == 0) { int height = matrixGray.rows(); if (Math.round(height * RELATIVE_FACE_SIZE) > 0) { absoluteFaceSize = Math.round(height * RELATIVE_FACE_SIZE); } } MatOfRect faces = new MatOfRect(); if (detectorFace != null) { if (matrixGray.height() > 0) { detectorFace.detectMultiScale(matrixGray, faces, 1.1, 2, 2, new Size(absoluteFaceSize, absoluteFaceSize), new Size()); } } return faces; } opencv.domain.FDInteractorImpl OpenCV face detection

Rendering graphics @RaulHernandezL Computer vision general purpose library #AR #MachineLearning
#droidconUK

@RaulHernandezL #AR #MachineLearning #droidconUK // computing eye areas as well
as splitting it Rect leftEyeArea = new Rect(face.x + face.width / 16 + (face.width - 2 * face.width / 16) / 2, (int) (face.y + (face.height / 4.5)), (face.width - 2 * face.width / 16) / 2, (int) (face.height / 3.0)); FaceDrawerOpenCV.drawEyesRectangles(rightEyeArea, leftEyeArea, matrixRGBA); opencv.domain.EyesDetectionInteractorImpl OpenCV draws initial eyes areas

@RaulHernandezL #AR #MachineLearning #droidconUK // computing eye areas as well
as splitting it Rect leftEyeArea = new Rect(face.x + face.width / 16 + (face.width - 2 * face.width / 16) / 2, (int) (face.y + (face.height / 4.5)), (face.width - 2 * face.width / 16) / 2, (int) (face.height / 3.0)); FaceDrawerOpenCV.drawEyesRectangles(rightEyeArea, leftEyeArea, matrixRGBA); opencv.domain.EyesDetectionInteractorImpl public static void drawIrisRectangle (Rect eyeTemplate , Mat matrixRgba) { Imgproc.rectangle(matrixRgba, eyeTemplate.tl() , eyeTemplate.br() , new Scalar(255, 0, 0, 255), 2); } Image in color OpenCV draws initial eyes areas

OpenCV face validation @RaulHernandezL Face (& eyes) detection AR markers
#AR #MachineLearning #droidconUK

@RaulHernandezL #AR #MachineLearning #droidconUK String methodForEyes; if (learnFrames < LEARN_FRAMES_LIMIT)
{ templateRight = buildTemplate(rightEyeArea, IRIS_MIN_SIZE, matrixGray, matrixRGBA, detectorEye); templateLeft = buildTemplate(leftEyeArea, IRIS_MIN_SIZE, matrixGray, matrixRGBA, detectorEye); learnFrames++; methodForEyes = "building Template with Detect multiscale, frame: " + learnFrames; } else { // Learning finished, use the new templates for template matching matchEye(rightEyeArea, templateRight, matrixGray, matrixRGBA); matchEye(leftEyeArea, templateLeft, matrixGray, matrixRGBA); methodForEyes = "match eye with Template, frame: " + learnFrames; } notifyEyesFound(methodForEyes); OpenCV eyes detection opencv.domain.EyesDetectionInteractorImpl

@RaulHernandezL #AR #MachineLearning #droidconUK Imgproc.matchTemplate(submatGray, builtTemplate, outputTemplateMat, Imgproc.TM_SQDIFF_NORMED); Core.MinMaxLocResult minMaxLocResult
= Core.minMaxLoc(outputTemplateMat); // when is difference in matching methods, the best match is max / min value matchLoc = minMaxLocResult.minLoc; Point matchLocTx = new Point(matchLoc.x + area.x, matchLoc.y + area.y); Point matchLocTy = new Point(matchLoc.x + builtTemplate.cols() + area.x, matchLoc.y + builtTemplate.rows() + area.y); FaceDrawerOpenCV.drawMatchedEye(matchLocTx, matchLocTy, matrixRGBA); opencv.domain.EyesDetectionInteractorImpl OpenCV template matching

= Core.minMaxLoc(outputTemplateMat); // when is difference in matching methods, the best match is max / min value matchLoc = minMaxLocResult.minLoc; Point matchLocTx = new Point(matchLoc.x + area.x, matchLoc.y + area.y); Point matchLocTy = new Point(matchLoc.x + builtTemplate.cols() + area.x, matchLoc.y + builtTemplate.rows() + area.y); FaceDrawerOpenCV.drawMatchedEye(matchLocTx, matchLocTy, matrixRGBA); opencv.domain.EyesDetectionInteractorImpl OpenCV minMaxLoc

= Core.minMaxLoc(outputTemplateMat); // when is difference in matching methods, the best match is max / min value matchLoc = minMaxLocResult.minLoc; Point matchLocTx = new Point(matchLoc.x + area.x, matchLoc.y + area.y); Point matchLocTy = new Point(matchLoc.x + builtTemplate.cols() + area.x, matchLoc.y + builtTemplate.rows() + area.y); FaceDrawerOpenCV.drawMatchedEye(matchLocTx, matchLocTy, matrixRGBA); opencv.render.FaceDrawerOpenCV OpenCV draw eye public static void drawMatchedEye(Point matchLocTx, Point matchLocTy, Mat matrixRgba) { Imgproc.rectangle(matrixRgba, matchLocTx, matchLocTy, new Scalar(255, 255, 0, 255)); }

OpenCV template matching @RaulHernandezL #AR #MachineLearning #droidconUK cv2.TM_SQDIFF Original picture
from: opencv 3.0 Documentation

OpenCV face validation @RaulHernandezL Accurate Eye detection AR markers #AR
#MachineLearning #droidconUK

@RaulHernandezL OpenCV UX #AR #MachineLearning #droidconUK

How do I start? @RaulHernandezL Native camera detection Camera API
Camera 2 API #AR #MachineLearning #droidconUK https://github.com/raulh82vlc/Image-Detection-Samples

Camera2 workflow @RaulHernandezL #AR #MachineLearning #droidconUK https://github.com/raulh82vlc/Image-Detection-Samples

Rendering graphics @RaulHernandezL Canvas rendering #AR #MachineLearning #droidconUK

Rendering graphics @RaulHernandezL OpenGL or external Graphics libraries #AR #MachineLearning
#droidconUK

private CameraCaptureSession.CaptureCallback captureCallback = new CameraCaptureSession.CaptureCallback() { … @Override public
void onCaptureProgressed(@NonNull CameraCaptureSession session, @NonNull CaptureRequest request, @NonNull CaptureResult captureResult) { detectFaces(captureResult); } @Override public void onCaptureCompleted(@NonNull CameraCaptureSession session, @NonNull CaptureRequest request, @NonNull TotalCaptureResult captureResult) { detectFaces(captureResult); } @RaulHernandezL #AR #MachineLearning #droidconUK Camera2 face detection camera2.presentation.FDCamera2Presenter

private void detectFaces(CaptureResult captureResult) { Integer mode = captureResult.get(CaptureResult.STATISTICS_FACE_DETECT_MODE); if
(isViewAvailable() && mode != null) { android.hardware.camera2.params.Face[] faces = captureResult.get(CaptureResult.STATISTICS_FACES); if (faces != null) { Log.i(TAG, "faces : " + faces.length + " , mode : " + mode); for (android.hardware.camera2.params.Face face : faces) { Rect faceBounds = face.getBounds(); // Once processed, the result is sent back to the View presenterView.onFaceDetected(mapCameraFaceToCanvas(faceBounds, face.getLeftEyePosition(), face.getRightEyePosition())); } } } } @RaulHernandezL #AR #MachineLearning #droidconUK Camera2 face detection camera2.presentation.FDCamera2Presenter

public class FaceDrawer extends View { ... @Override protected void
onDraw(Canvas canvas) { super.onDraw(canvas); if (face != null) { Square faceShape = face.getFaceShape(); int w = faceShape.getWidth(); int h = faceShape.getHeight(); drawFaceMarker(canvas, faceShape, w, h); // Bitmap AR Super Sayan drawBitmapAR(canvas, drawableStore.get(KEY_BITMAP_HEAD), w, h, (int) faceShape.getStart().getxAxis() - (w / 2), (int) faceShape.getStart().getyAxis() - (int)(h * 1.5)); } } @RaulHernandezL #AR #MachineLearning #droidconUK camera2.render.FaceDrawer Camera2 face rendering

private void drawBitmapAR(Canvas canvas, Drawable drawable, int w, int h,
int x, int y) { if (drawable != null) { int widthGraphic = drawable.getIntrinsicWidth(); int heightGraphic = drawable.getIntrinsicHeight(); setTransformation(w, h, x, y, widthGraphic, heightGraphic); canvas.setMatrix(transformation); drawable.draw(canvas); } } @RaulHernandezL #AR #MachineLearning #droidconUK camera2.render.FaceDrawer Camera2 face rendering

private void drawBitmapAR(Canvas canvas, Drawable drawable, int w, int h,
int x, int y) { if (drawable != null) { int widthGraphic = drawable.getIntrinsicWidth(); int heightGraphic = drawable.getIntrinsicHeight(); setTransformation(w, h, x, y, widthGraphic, heightGraphic); canvas.setMatrix(transformation); drawable.draw(canvas); } } private void setTransformation(int w, int h, int x, int y, int widthGraphic, int heightGraphic) { MeasuresUI measures = TransformationsHelper.calcMeasures(w, h, viewWidth, viewHeight, x, y, widthGraphic, heightGraphic); transformation.setScale(measures.getScale(), measures.getScale()); transformation.postTranslate(measures.getDx(), measures.getDy()); transformation.postConcat(transformation); } @RaulHernandezL #AR #MachineLearning #droidconUK camera2.render.FaceDrawer Camera2 face rendering

@RaulHernandezL Face detection (Camera2) Camera2 face validation #AR #MachineLearning #droidconUK

@RaulHernandezL AR marker (Paint) with Canvas on a View #AR
#MachineLearning #droidconUK Camera2 face validation

Camera2 UX @RaulHernandezL #AR #MachineLearning #droidconUK

Can you see any commonalities? @RaulHernandezL #AR #MachineLearning #droidconUK

Can you see any commonalities? @RaulHernandezL #AR #MachineLearning #droidconUK Clear
and separated features

and separated features A defined Architecture

and separated features A defined Architecture UI driven

Did we finish this MVP? @RaulHernandezL #AR #MachineLearning #droidconUK

@RaulHernandezL #AR #MachineLearning #droidconUK Detecting nose or mouth, or other
shapes to render. Improvement of graphics user experience (UX) for instance using a kalman filter, or using tracking algorithms like “good features to track”... Extensions for detection & rendering

Is anything missed? @RaulHernandezL #AR #MachineLearning #droidconUK Start planning Face
recognition

Recognition workflow @RaulHernandezL #AR #MachineLearning #droidconUK

Machine learning @RaulHernandezL #AR #MachineLearning #droidconUK Microsoft Azure Machine Learning
cheat sheet

Deep learning @RaulHernandezL #AR #MachineLearning #droidconUK Adapted from the original
image from Max Tegmark - MIT Connections between physics and deep learning on YouTube Label Image

Convolutional Neural Network (CNN) @RaulHernandezL #AR #MachineLearning #droidconUK Original image
from Mathworks Discovery section

Recognition Cloud APIs @RaulHernandezL #AR #MachineLearning #droidconUK a. Google Cloud
Vision (Only Detection) b. Amazon Rekognition c. Microsoft Azure Cognitive Services Azure Cognitive services for face API Cloud Google Vision API Amazon Rekognition API

Train a model from scratch @RaulHernandezL #AR #MachineLearning #droidconUK

Pre-trained model @RaulHernandezL #AR #MachineLearning #droidconUK

Using a Pre-trained model @RaulHernandezL #AR #MachineLearning #droidconUK Transfer learning
Pic 1 from Tate museum Pic 2 from Mirror Celebrity news Pic 3 from Eccles photographs blog

Using a Pre-trained model @RaulHernandezL #AR #MachineLearning #droidconUK Daj´s Using
a pre-trained Tensorflow model on Android Original diagram from Jalammar blog -> Supercharging android apps using Tensorflow

Using a Pre-trained model @RaulHernandezL #AR #MachineLearning #droidconUK TensorFlow now
bundles the native binaries and Java interface.

Using a Pre-trained model @RaulHernandezL #AR #MachineLearning #droidconUK TensorFlow now
bundles the native binaries and Java interface. JAR -> libandroid_tensorflow_interface_java.jar or native binaries -> libtensorflow_inference.so dependencies { compile 'org.tensorflow:tensorflow-android:1.2.0' }

Using a Pre-trained model @RaulHernandezL #AR #MachineLearning #droidconUK Adding to
app/src/main/assets: Model graph -> .pb & graph labels -> .txt

Using a Pre-trained model @RaulHernandezL #AR #MachineLearning #droidconUK public static
TensorFlowFaceClassifier buildFaceClassifier(AssetManager assetManager, String modelFileName, String labelFileName, int inputSize, String inputName, String outputName) { TensorFlowFaceClassifier faceClassifier = new TensorFlowFaceClassifier(); … // Set input and output names // For each line read from labelFileName, add its label into memory faceClassifier.addLabel(lineOnFile); // String values … faceClassifier.inferenceInterface = new TensorFlowInferenceInterface(assetManager, modelFileName); // The shape of the output is [N, NUM_CLASSES], where N is the batch size. int numClasses = (int) faceClassifier.inferenceInterface.graph().operation(outputName).output(0).shape().size(1); faceClassifier.inputSize = inputSize; faceClassifier.outputNames = new String[]{outputName}; faceClassifier.outputs = new float[numClasses]; return faceClassifier; } .facerecognition.TensorFlowFaceClassifier (AndroidTensorFlowMNISTExample modified)

TensorFlowFaceClassifier buildFaceClassifier(AssetManager assetManager, String modelFileName, String labelFileName, int inputSize, String inputName, String outputName) { TensorFlowFaceClassifier faceClassifier = new TensorFlowFaceClassifier(); … // Set input and output names // For each line read from labelFileName, add its label into memory faceClassifier.addLabel(lineOnFile); // String values … faceClassifier.inferenceInterface = new TensorFlowInferenceInterface(assetManager, modelFileName); // The shape of the output is [N, NUM_CLASSES], where N is the batch size. int numClasses = (int) faceClassifier.inferenceInterface.graph().operation(outputName).output(0).shape().size(1); faceClassifier.inputSize = inputSize; faceClassifier.outputNames = new String[]{outputName}; faceClassifier.outputs = new float[numClasses]; return faceClassifier; } Output layer size .facerecognition.TensorFlowFaceClassifier (AndroidTensorFlowMNISTExample modified)

TensorFlowFaceClassifier buildFaceClassifier(AssetManager assetManager, String modelFileName, String labelFileName, int inputSize, String inputName, String outputName) { TensorFlowFaceClassifier faceClassifier = new TensorFlowFaceClassifier(); … // Set input and output names // For each line read from labelFileName, add its label into memory faceClassifier.addLabel(lineOnFile); // String values … faceClassifier.inferenceInterface = new TensorFlowInferenceInterface(assetManager, modelFileName); // The shape of the output is [N, NUM_CLASSES], where N is the batch size. int numClasses = (int) faceClassifier.inferenceInterface.graph().operation(outputName).output(0).shape().size(1); faceClassifier.inputSize = inputSize; faceClassifier.outputNames = new String[]{outputName}; faceClassifier.outputs = new float[numClasses]; return faceClassifier; } Confidence results .facerecognition.TensorFlowFaceClassifier (AndroidTensorFlowMNISTExample modified)

TensorFlowFaceClassifier buildFaceClassifier(AssetManager assetManager, String modelFileName, String labelFileName, int inputSize, String inputName, String outputName) { TensorFlowFaceClassifier faceClassifier = new TensorFlowFaceClassifier(); … // Set input and output names // For each line read from labelFileName add its label into memory faceClassifier.addLabel(lineOnFile); // String values … faceClassifier.inferenceInterface = new TensorFlowInferenceInterface(assetManager, modelFileName); // The shape of the output is [N, NUM_CLASSES], where N is the batch size. int numClasses = (int) faceClassifier.inferenceInterface.graph().operation(outputName).output(0).shape().size(1); faceClassifier.inputSize = inputSize; faceClassifier.outputNames = new String[]{outputName}; faceClassifier.outputs = new float[numClasses]; return faceClassifier; } .facerecognition.TensorFlowFaceClassifier (AndroidTensorFlowMNISTExample modified)

Using a Pre-trained model @RaulHernandezL #AR #MachineLearning #droidconUK .facerecognition.TensorFlowFaceClassifier (AndroidTensorFlowMNISTExample
modified) public List<Recognition> recognizeFaceFromImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed(inputName, pixels, new long[]{inputSize * inputSize}); // Run the inference call inferenceInterface.run(outputNames, runStatus); // Copy the output Tensor back into the output array inferenceInterface.fetch(outputName, outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { if (outputs[i] > THRESHOLD) { // > 0.1f // the following priority queue is sorted by resulting confidence priorityQueue.add(new Recognition( "" + i, labels.size() > i ? labels.get(i) : "unknown", outputs[i], null)); } } return getRecognitionsListFromQueue(priorityQueue); } .facerecognition.TensorFlowFaceClassifier (AndroidTensorFlowMNISTExample modified)

modified) public List<Recognition> recognizeFaceFromImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed(inputName, pixels, new long[]{inputSize * inputSize}); // Run the inference call inferenceInterface.run(outputNames, runStatus); // Copy the output Tensor back into the output array inferenceInterface.fetch(outputName, outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { if (outputs[i] > THRESHOLD) { // > 0.1f // the following priority queue is sorted by resulting confidence priorityQueue.add(new Recognition( "" + i, labels.size() > i ? labels.get(i) : "unknown", outputs[i])); } } return getRecognitionsListFromQueue(priorityQueue); } Extracted values of confidence .facerecognition.TensorFlowFaceClassifier (AndroidTensorFlowMNISTExample modified)

modified) public List<Recognition> recognizeFaceFromImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed(inputName, pixels, new long[]{inputSize * inputSize}); // Run the inference call inferenceInterface.run(outputNames, runStatus); // Copy the output Tensor back into the output array inferenceInterface.fetch(outputName, outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { if (outputs[i] > THRESHOLD) { // > 0.1f // the following priority queue is sorted by resulting confidence priorityQueue.add(new Recognition( "" + i, labels.size() > i ? labels.get(i) : "unknown", outputs[i])); } } return getRecognitionsListFromQueue(priorityQueue); } Id Label associated to this recognition Confidence .facerecognition.TensorFlowFaceClassifier (AndroidTensorFlowMNISTExample modified)

modified) public List<Recognition> recognizeFaceFromImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed(inputName, pixels, new long[]{inputSize * inputSize}); // Run the inference call inferenceInterface.run(outputNames, runStatus); // Copy the output Tensor back into the output array inferenceInterface.fetch(outputName, outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { if (outputs[i] > THRESHOLD) { // > 0.1f // the following priority queue is sorted by resulting confidence priorityQueue.add(new Recognition( "" + i, labels.size() > i ? labels.get(i) : "unknown", outputs[i], null)); } } return getRecognitionsListFromQueue(priorityQueue); } .facerecognition.TensorFlowFaceClassifier (AndroidTensorFlowMNISTExample modified)

Output @RaulHernandezL #AR #MachineLearning #droidconUK

Face Recognition @RaulHernandezL #AR #MachineLearning #droidconUK Expected status: Face is
recognized Use case image taken when using: Qualeams Android Face Recognition with Deep Learning Test Framework

Conclusions @RaulHernandezL #MVP #droidconUK

Conclusions @RaulHernandezL #MVP #droidconUK Make something simple (a prototype) means
take right decisions on time

Conclusions @RaulHernandezL #MVP #droidconUK Make something functional means we have
enough insights, time and learnt lessons to build something ready to go

Conclusions @RaulHernandezL #MVP #droidconUK Make something extendable means we have
made an effort to introduce best practices to create a maintainable product

References @RaulHernandezL Image Detection & AR Presentation Samples: https://github.com/raulh82vlc/Image-Detection-Samples OpenCV
OpenCV tutorial http://www.learnopencv.com/image-recognition-and-object-detection-part1/ OpenCV Sample https://github.com/opencv/opencv/tree/master/samples/android/face-detection OpenCV for Secret Agents - Joseph Howse https://www.packtpub.com/application-development/opencv-secret-agents Camera 2 Camera 2 Sample Basic https://github.com/googlesamples/android-Camera2Basic Camera 2 Reference https://developer.android.com/reference/android/hardware/camera2/package-summary.html Camera 2 Introduction on Dev.Bytes https://youtu.be/Xtp3tH27OFs #AR #MachineLearning #droidconUK

References @RaulHernandezL TensorFlow Deep learning library https://github.com/Qualeams/Android-Face-Recognition-with-Deep-Learning-Library Mindorks MNIST example
https://github.com/MindorksOpenSource/AndroidTensorFlowMNISTExample ML LetterPredictor Android https://github.com/mccorby/ML-LetterPredictor-Android TensorFlow image retraining https://www.tensorflow.org/tutorials/image_retraining Applied TensorFlow for Android apps https://speakerdeck.com/daj/applied-tensorflow-for-android-apps Building Mobile Apps with TensorFlow - Pete Warden Machine learning Deep Learning in a Nutshell – what it is, how it works, why care? https://www.kdnuggets.com/2015/01/deep-learning-explanation-what-how-why.html Transfer learning with Keras https://medium.com/towards-data-science/transfer-learning-using-keras-d804b2e04ef8 Neural networks deep learning https://medium.com/machine-learning-for-humans/neural-networks-deep-learning-cdad8aeae49b #AR #MachineLearning #droidconUK

Thanks! QA #droidconUK @RaulHernandezL raulh82vlc raul.h82

Building a "Minimum Viable Product" (MVP) with ...

Building a "Minimum Viable Product" (MVP) with Face recognition and AR in Android @ Droidcon London 2017

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