Detecting face landmarks points in android

Question

I am developing app in which I need to get face landmarks points on a cam like mirror cam or makeup cam. I want it to be available for iOS too. Please guide me for a robust solution. I have used Dlib and Luxand.

DLIB: https://github.com/tzutalin/dlib-android-app

Luxand: http://www.luxand.com/facesdk/download/

Dlib is slow and having a lag of 2 sec approximately (Please look at the demo video on the git page) and luxand is ok but it's paid. My priority is to use an open source solution. I have also use the Google vision but they are not offering much face landmarks points. So please give me a solution to make the the dlib to work fast or any other option keeping cross-platform in priority. Thanks in advance.

Answer 1

You can make Dlib detect face landmarks in real-time on Android (20-30 fps) if you take a few shortcuts. It's an awesome library.

Initialization

Firstly you should follow all the recommendations in Evgeniy's answer, especially make sure that you only initialize the frontal_face_detector and shape_predictor objects once instead of every frame. The frontal_face_detector will initialize faster if you deserialize it from a file instead of using the get_serialized_frontal_faces() function. The shape_predictor needs to be initialized from a 100Mb file, and takes several seconds. The serialize and deserialize functions are written to be cross-platform and perform validation on the data, which is robust but makes it quite slow. If you are prepared to make assumptions about endianness you can write your own deserialization function that will be much faster. The file is mostly made up of matrices of 136 floating point values (about 120000 of them, meaning 16320000 floats in total). If you quantize these floats down to 8 or 16 bits you can make big space savings (e.g. you can store the min value and (max-min)/255 as floats for each matrix and quantize each separately). This reduces the file size down to about 18Mb and it loads in a few hundred milliseconds instead of several seconds. The decrease in quality from using quantized values seems negligible to me but YMMV.

Face Detection

You can scale the camera frames down to something small like 240x160 (or whatever, keeping aspect ratio correct) for faster face detection. It means you can't detect smaller faces but it might not be a problem depending on your app. Another more complex approach is to adaptively crop and resize the region you use for face detections: initially check for all faces in a higher res image (e.g. 480x320) and then crop the area +/- one face width around the previous location, scaling down if need be. If you fail to detect a face one frame then revert to detecting the entire region the next one.

Face Tracking

For faster face tracking, you can run face detections continuously in one thread, and then in another thread, track the detected face(s) and perform face feature detections using the tracked rectangles. In my testing I found that face detection took between 100 - 400ms depending on what phone I used (at about 240x160), and I could do 7 or 8 face feature detections on the intermediate frames in that time. This can get a bit tricky if the face is moving a lot, because when you get a new face detection (which will be from 400ms ago), you have to decide whether to keep tracking from the new detected location or the tracked location of the previous detection. Dlib includes a correlation_tracker however unfortunately I wasn't able to get this to run faster than about 250ms per frame, and scaling down the resolution (even drastically) didn't make much of a difference. Tinkering with internal parameters produced increase speed but poor tracking. I ended up using a CAMShift tracker based on the chroma UV planes of the preview frames, generating the color histogram based on the detected face rectangles. There is an implementation of CAMShift in OpenCV, but it's also pretty simple to roll your own.

Hope this helps, it's mostly a matter of picking the low hanging fruit for optimization first and just keep going until you're happy it's fast enough. On a Galaxy Note 5 Dlib does face+feature detections at about 100ms, which might be good enough for your purposes even without all this extra complication.

Answer 2

Dlib is fast enough for most cases. The most of processing time is taken to detect face region on image and its slow because modern smartphones are producing high-resolution images (10MP+)

Yes, face detection can take 2+ seconds on 3-5MP image, but it tries to find very small faces of 80x80 pixels size. I am really sure, that you dont need such small faces on high resolution images and the main optimization here is to reduce the size of image before finding faces.

After the face region is found, the next step - face landmarks detection is extremely fast and takes < 3 ms for one face, this time does not depend on resolution.

dlib-android port is not using dlib's detector the right way for now. Here is a list of recommendations how to make dlib-android port work much faster: https://github.com/tzutalin/dlib-android/issues/15

Its very simple and you can implement it yourself. I am expecting performance gain about 2x-20x

Answer 3

I am currently working with the Google Vision API and it seems to be able to detect landmarks out of the box. Check out the FaceTracker here:

google face tracker

This solution should detect the face, happiness, and left and right eye as is. For other landmarks, you can call the getLandmarks on a Face and it should return everything you need (thought I have not tried it) according to their documentation: Face reference

Answer 4

Apart from OpenCV and Google Vision, there are widely-available web services like Microsoft Cognitive Services. The advantage is that it would be completely platform-independent, which you've listed as a major design goal. I haven't personally used them in an implementation yet but based on playing with their demos for awhile they seem quite powerful; they're pretty accurate and can offer quite a few details depending on what you want to know. (There are similar solutions available from other vendors as well by the way).

The two major potential downsides to something like that are the potential for added network traffic and API pricing (depending on how heavily you'll be using them).

Pricing-wise, Microsoft currently offers up to 5,000 transactions a month for free with added transactions beyond that being some fraction of a penny (depending on traffic, you can actually get a discount for high volume), but if you're doing, for example, millions of transactions per month the fees can start adding up surprisingly quickly. This is actually a fairly typical pricing model; before you select a vendor or implement this kind of a solution make sure you understand how they're going to charge you and how much you're likely to end up paying and how much you could be paying if you scale your user base. Depending on your traffic and business model it could be either very reasonable or cost-prohibitive.

The added network traffic may or may not be a problem depending on how your app is written and how much data you're sending. If you can do the processing asynchronously and be guaranteed reasonably fast Wi-Fi access that obviously wouldn't be a problem but unfortunately you may or may not have that luxury.