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go-face Build Status GoDoc

go-face implements face recognition for Go using dlib, a popular machine learning toolkit. Read Face recognition with Go article for some background details if you're new to FaceNet concept.


To compile go-face you need to have dlib (>= 19.10) and libjpeg development packages installed.

Ubuntu 18.10+, Debian sid

Latest versions of Ubuntu and Debian provide suitable dlib package so just run:

# Ubuntu
sudo apt-get install libdlib-dev libblas-dev liblapack-dev libjpeg-turbo8-dev
# Debian
sudo apt-get install libdlib-dev libblas-dev liblapack-dev libjpeg62-turbo-dev


Make sure you have Homebrew installed.

brew install dlib


Make sure you have MSYS2 installed.

  1. Run MSYS2 MSYS shell from Start menu
  2. Run pacman -Syu and if it asks you to close the shell do that
  3. Run pacman -Syu again
  4. Run pacman -S mingw-w64-x86_64-gcc mingw-w64-x86_64-dlib
    1. If you already have Go and Git installed and available in PATH uncomment set MSYS2_PATH_TYPE=inherit line in msys2_shell.cmd located in MSYS2 installation folder
    2. Otherwise run pacman -S mingw-w64-x86_64-go git
  5. Run MSYS2 MinGW 64-bit shell from Start menu to compile and use go-face

Other systems

Try to install dlib/libjpeg with package manager of your distribution or compile from sources. Note that go-face won't work with old packages of dlib such as libdlib18. Alternatively create issue with the name of your system and someone might help you with the installation process.


Currently shape_predictor_5_face_landmarks.dat, mmod_human_face_detector.dat and dlib_face_recognition_resnet_model_v1.dat are required. You may download them from go-face-testdata repo:



To use go-face in your Go code:

import ""

To install go-face in your $GOPATH:

go get

For further details see GoDoc documentation.


package main

import (


// Path to directory with models and test images. Here it's assumed it
// points to the <> clone.
const dataDir = "testdata"

var (
	modelsDir = filepath.Join(dataDir, "models")
	imagesDir = filepath.Join(dataDir, "images")

// This example shows the basic usage of the package: create an
// recognizer, recognize faces, classify them using few known ones.
func main() {
	// Init the recognizer.
	rec, err := face.NewRecognizer(modelsDir)
	if err != nil {
		log.Fatalf("Can't init face recognizer: %v", err)
	// Free the resources when you're finished.
	defer rec.Close()

	// Test image with 10 faces.
	testImagePristin := filepath.Join(imagesDir, "pristin.jpg")
	// Recognize faces on that image.
	faces, err := rec.RecognizeFile(testImagePristin)
	if err != nil {
		log.Fatalf("Can't recognize: %v", err)
	if len(faces) != 10 {
		log.Fatalf("Wrong number of faces")

	// Fill known samples. In the real world you would use a lot of images
	// for each person to get better classification results but in our
	// example we just get them from one big image.
	var samples []face.Descriptor
	var cats []int32
	for i, f := range faces {
		samples = append(samples, f.Descriptor)
		// Each face is unique on that image so goes to its own category.
		cats = append(cats, int32(i))
	// Name the categories, i.e. people on the image.
	labels := []string{
		"Sungyeon", "Yehana", "Roa", "Eunwoo", "Xiyeon",
		"Kyulkyung", "Nayoung", "Rena", "Kyla", "Yuha",
	// Pass samples to the recognizer.
	rec.SetSamples(samples, cats)

	// Now let's try to classify some not yet known image.
	testImageNayoung := filepath.Join(imagesDir, "nayoung.jpg")
	nayoungFace, err := rec.RecognizeSingleFile(testImageNayoung)
	if err != nil {
		log.Fatalf("Can't recognize: %v", err)
	if nayoungFace == nil {
		log.Fatalf("Not a single face on the image")
	catID := rec.Classify(nayoungFace.Descriptor)
	if catID < 0 {
		log.Fatalf("Can't classify")
	// Finally print the classified label. It should be "Nayoung".

Run with:

mkdir -p ~/go && cd ~/go  # Or cd to your $GOPATH
mkdir -p src/go-face-example && cd src/go-face-example
git clone testdata
edit main.go  # Paste example code
go get && go run main.go


To fetch test data and run tests:

make test


How to improve recognition accuracy

There are few suggestions:

  • Try CNN recognizing
  • Try different tolerance values of ClassifyThreshold
  • Try different size/padding/jittering values of NewRecognizerWithConfig
  • Provide more samples of each category to SetSamples if possible
  • Implement better classify heuristics (see
  • Train network (dlib_face_recognition_resnet_model_v1.dat) on your own test data


go-face is licensed under CC0.