SDK Developer Guide Release 3.2

Using the SDK

The SDK is distributed as an Android “.aar” archive. It can be included in an app by declaring a dependency on the SDK in the app’s build.gradle file.

1. Add Affectiva’s repository as a remote repo for your application. This tells gradle that it should scan the Affectiva software distribution site for the app’s dependencies. Add a declaration to the app’s root build.gradle file:
allprojects {
    repositories {
        maven {
            url ""

For an example please see the AffdexMe sample app.

2. Add a dependency declaration to your app’s build.gradle file. This will pick up the most recent bug fix release in the 3.x series.
dependencies {
    compile ''

For an example please see the AffdexMe sample app’s app-level build.gradle file.

3. Add a few necessary declarations to your app’s manifest. Therefore, the AndroidManifest.xml needs to include the following permissions:

First, the SDK requires access to external storage on the Android device.

<uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE" />

Also, the SDK uses access to the internet to communicate anonymized usage data.

<uses-permission android:name="android.permission.INTERNET" />
<uses-permission android:name="android.permission.ACCESS_NETWORK_STATE" />

Additionally, if you use the CameraDetector, then you will need to add permission to access the camera:

<uses-permission android:name="android.permission.CAMERA" />

Second, the SDK is required by Android’s packaging system to declare a couple of parameters, android:allowBackup and android:label, that you may want to override. To do this, add the “tools” namespace to your manifest’s document element:

<manifest xmlns:android=""

Then add a “tools:replace” attribute to the “application” element:


You can then add your own “allowBackup” and “label” attributes:

4. Capture and analyze faces

Facial images can be captured from different sources. For each of the different sources, the SDK defines a detector class that can handle processing images acquired from that source:

5. Check out sample applications on GitHub

Sample applications for processing videos, and connecting to the camera are available for cloning on our GitHub repository.

Class documentation

  • class docs: [HTML]

Requirements & Dependencies

  • Processor, Quad-core 1.5 GHz Cortex-A53
  • RAM, 1 GB
Tracking multiple faces

As of v3.0, the SDK exposes a parameter max_faces in the detectors constructor to specify the maximum number of faces to look for in an image. For the realtime use cases, to achieve a high accuracy and processing throughput (20+ processed frames per second), the SDK requires a CPU thread per face.

On a recent dual core machine, we can track up to 3 people in parallel with all the facial expressions, emotions and appearance metrics enabled.

If the number of faces tracked is greater than the number of available CPU threads on the machine, they will all be tracked, but at a cost of the processing frame rate. Therefore, make sure to plan for providing enough hardware power for the number of faces they are expecting to track with each camera.

  • A device running Android API 16 or above.
  • Java 1.7 or above is required on your development machine.
Supported operating systems
  • Android 4.4 and above