Detect faces in images

As mentioned this Robot enables you to detect and extract human faces from your images. You can also specify a padding around the extracted faces.

It works well together with the /image/resize Robot to bring the full power of resized and optimized images to your website or app.

Parameters

• use

  String / Array of Strings / Object required

  Specifies which Step(s) to use as input.

  • You can pick any names for Steps except ":original" (reserved for user uploads handled by Transloadit)

  • You can provide several Steps as input with arrays:

    "use": [
      ":original",
      "encoded",
      "resized"
    ]
    

  That’s likely all you need to know about use, but you can view advanced use cases:

  › Advanced use cases

  • Step bundling. Some Robots can gather several Step results for a single invocation. For example, the /file/compress Robot would normally create one archive for each file passed to it. If you'd set bundle_steps to true, however, it will create one archive containing all the result files from all Steps you give it. To enable bundling, provide an object like the one below to the use parameter:

    "use": {
      "steps": [
        ":original",
        "encoded",
        "resized"
      ],
      "bundle_steps": true
    }
    

    This is also a crucial parameter for the /video/adaptive Robot, otherwise you'll generate 1 playlist for each viewing quality.
    Keep in mind that all input Steps must be present in your Template. If one of them is missing (for instance it is rejected by a filter), no result is generated because the Robot waits indefinitely for all input Steps to be finished.

    Here’s a demo that showcases Step bundling.

  • Group by original. Sticking with the /file/compress Robot example, you can set group_by_original to true, in order to create a separate archive for each of your uploaded or imported files, instead of creating one archive containing all originals (or one per resulting file). This is important for for the /media/playlist Robot where you'd typically set:

    "use": {
      "steps": [
        "segmented"
      ],
      "bundle_steps": true,
      "group_by_original": true
    }
    

  • Fields. You can be more discriminatory by only using files that match a field name by setting the fields property. When this array is specified, the corresponding Step will only be executed for files submitted through one of the given field names, which correspond with the strings in the name attribute of the HTML file input field tag for instance. When using a back-end SDK, it corresponds with myFieldName1 in e.g.: $transloadit->addFile('myFieldName1', './chameleon.jpg').

    This parameter is set to true by default, meaning all fields are accepted.

    Example:

    "use": {
      "steps": [ ":original" ],
      "fields": [ "myFieldName1" ]
    }
    

  • Use as. Sometimes Robots take several inputs. For instance, the /video/merge Robot can create a slideshow from audio and images. You can map different Steps to the appropriate inputs.

    Example:

    "use": {
      "steps": [
        { "name": "audio_encoded", "as": "audio" },
        { "name": "images_resized", "as": "image" }
      ]
    }
    

    Sometimes the ordering is important, for instance, with our concat Robots. In these cases, you can add an index that starts at 1. You can also optionally filter by the multipart field name. Like in this example, where all files are coming from the same source (end-user uploads), but with different <input> names:

    Example:

    "use": {
      "steps": [
        { "name": ":original", "fields": "myFirstVideo", "as": "video_1" },
        { "name": ":original", "fields": "mySecondVideo", "as": "video_2" },
        { "name": ":original", "fields": "myThirdVideo", "as": "video_3" }
      ]
    }
    

    For times when it is not apparent where we should put the file, you can use Assembly Variables to be specific. For instance, you may want to pass a text file to the /image/resize Robot to burn the text in an image, but you are burning multiple texts, so where do we put the text file? We specify it via ${use.text_1}, to indicate the first text file that was passed.

    Example:

    "watermarked": {
      "robot": "/image/resize",
      "use"  : {
        "steps": [
          { "name": "resized", "as": "base" },
          { "name": "transcribed", "as": "text" },
        ],
      },
      "text": [
        {
          "text"  : "Hi there",
          "valign": "top",
          "align" : "left",
        },
        {
          "text"    : "From the 'transcribed' Step: ${use.text_1}",
          "valign"  : "bottom",
          "align"   : "right",
          "x_offset": 16,
          "y_offset": -10,
        }
      ]
    }
    

• crop

  Boolean ⋅ default: false

  Determine if the detected faces should be extracted. If this option is set to false, then the Robot returns the input image again, but with the coordinates of all detected faces attached to file.meta.faces in the result JSON. If this parameter is set to true, the Robot will output all detected faces as images.

• crop_padding

  String ⋅ default: "5px"

  Specifies how much padding is added to the extracted face images if crop is set to true. Values can be in px (pixels) or % (percentage of the width and height of the particular face image).

• format

  String ⋅ default: "preserve"

  Determines the output format of the extracted face images if crop is set to true.

  The default value "preserve" means that the input image format is re-used. Valid values are "jpg", "png", "tiff" and "preserve".

• min_confidence

  Integer(0-100) ⋅ default: 70

  Specifies the minimum confidence that a detected face must have. Only faces which have a higher confidence value than this threshold will be included in the result.

• faces

  String / Integer ⋅ default: "each"

  Determines which of the detected faces should be returned. Valid values are:

  • "each" — each face is returned individually.
  • "max-confidence" — only the face with the highest confidence value is returned.
  • "max-size" — only the face with the largest area is returned.
  • "group" — all detected faces are grouped together into one rectangle that contains all faces.
  • any integer — the faces are sorted by their top-left corner and the integer determines the index of the returned face. Be aware the values are zero-indexed, meaning that faces: 0 will return the first face. If no face for a given index exists, no output is produced.

  For the following examples, the input image is:

  

  
  faces: "each" applied:

  

  
  faces: "max-confidence" applied:

  

  
  faces: "max-size" applied:

  

  
  faces: "group" applied:

  

  
  faces: 0 applied:

  

Demos

• Detect faces in images
• Extract all the faces into a single image

Related blog posts

• Our Smart CDN now supports Face Detection! June 21, 2022
• 🧠 Tech Preview of our new AI bots February 17, 2020
• Showcase of the new faces parameter for the /image/facedetect Robot October 25, 2019
• Adding support for image face detection February 5, 2016