I'm making system for emotion detection on Android mobile phone. I'm using OpenCV's Cascades (LBP's or Haars) to find face, eyes, mouth areas etc. What I have observed till now that accuracy isn't stable. There are situations where I can't find eye or I have "additional faces" in the background due to very slight change of light. What I wanted to ask is:
1) Is Haar Cascade more Accurate than LBP?
2) Is there any good method for increasing accuracy of detection? Like find face/eyes etc on binarized image, or use some edge detection filter, saturation, anything else?
you can try Microsoft API for face emotion detection ..i am trying in my project so..result is best..try this link
https://www.microsoft.com/cognitive-services/en-us/emotion-api
sometimes HAAR or LBP will not get a good enough result for a face detection system. if you want to get more good acc. i think you can try to using STASM
it base on opencv and using Haar to detect face and landmark. something others you can also try YOLO Face detection
if you want to build your own face detection system just base on Haar or LBP and make them get a good result, maybe you will need to using the LBP to find out the face faster and train a CNN model to get the last good result, it can make your system to detect faces in realtime. as i know, the SEETAFACE is using this way to make a realtime faces detection.
Related
So I want to count how many people appear in a facebook profile picture.
Typically there are 0-2 people (sometimes there are 4-5+ but that's more rare).
A sample dataset (and a few tries using python) can be found here:
https://github.com/yoniker/FaceDetect
I've tried different methods, none of them give reasonable results (all of those methods are wrong most of the time),I've tried the following:
-Face detection- http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html
It usually doesn't find anyone (that happens at around 75% of the pictures)- and I have tried different Haar filters and parameters.
-Pedestrian Detection http://www.pyimagesearch.com/2015/11/09/pedestrian-detection-opencv/
Again it doesn't find people most of the time.
OpenFace:Probably this face recognition algo doesn't truly help with face detection (see https://groups.google.com/forum/#!topic/cmu-openface/X6erXKckk0Q).
And finally I've looked at different StackOverflow questions such as
Count the number of people in the video but none of them are relevant!
I've tried for half a day now- so help will be super appreciated!!
For me, dlib has given better results than using OpenCV's haar face detector. It has python bindings too. You can find quick-start code to do face detection here.
It would be possible to help better if you post an Image in which faces are not detected properly.
Having said that, to improve face detection apart from using dlib, you can experiment with these ideas:
Use histogram equalisation(equalizeHist on opencv) on gray scale image before passing it to face detector. (i.e preprocess your images)
If faces are tilted to left or right, more often face detection fails. To solve this rotate the images in steps of 5 degrees upto 30 degrees and apply face detection. At each rotation you might detect new faces.
Most face detectors which are not using deep learning detect mostly frontal faces. Not much could be done about this apart from using deep learning or train your own side profile face detector using HOG or HAAR features.
Hope this helps you to improve your face detection.
There's always the cascade classifier in OpenCV for all your face detection needs. If you could feed it with some nice features it would give you all the results.
I am looking for algorithms/publications on face detection. There are plenty in the web. But my scenario is somewhat specialized. I want to detect faces accurately in images taken by wearable devices (e.g. narrative clips), so there will be motion blur, and image quality will not be that good. I want to detect faces that are within 15 feet of the camera accurately. Next goal is to estimate the pose, primarily to find out if the person is looking toward the camera ( or better looking at the camera owner).
Any suggestion?
My go to for this would either be a deep-learning framework using convolutional layers for pixel classification, or K-means/ K-Nearest Neighbour algorithm.
This does depend on your data, however. From your post I am assuming that your data isn't labelled? meaning you are unable to feed in the 'truth' to the algorithm for classification.
you could perhaps use a CNN (convolutional neural network) for pixel classification (image segmentation) which should identify the location of a person. given this, perhaps you could run a 'local' CNN i a region close to the face identified to classify the region the body is located in as a certain pose.
This would probably be my first take on the problem but would depend on the exact structure of your data, and the structure of your labels (if you have any).
I have to say it does sound like a fun project!
I found OpenCV's Haar Cascades for Face Detection pretty accurate and robust for motion blur and "live" face recognition.
I'm saying that because I used them for implementing an Eye-Tracker in C++ with a laptop webcam (whose resolution was not excellent and motion blur was naturally always present).
They work in multiresolution and are therefore able to detect faces of any size, but you can easily tune them for your distance of interest.
They might not be your final optimal solution, but since they are already implemented and come with the OpenCV package, they could constitute a good starting point.
I have been given an image with rgb channels. I only want to see the persons face. How would I do that? Are neural nets used for this? If so, are there existing data files from neural nets that have already done the processing?
Since your questions is tagged with OpenCV, I will assume that you are looking for a solution within this library.
The first step is to find the faces. For this, use one of the cascade object detectors that are available: either the Viola-Jones one or the LBP one.
OpenCV comes with cascades trained for face detection for each of these detectors.
Then, it depends if getting a bounding box is enough or not.
If you need something more accurate, then you can:
[coarse face] use a skin color detector inside the face bounding box to get a finer face estimate, binarize the image and finally close the face shape using morphological filtering;
[fine face contour] use something like a grabcut procedure to get a pixel-accurate contour. You can initialize the grabcut with borders of the bounding box as background and center part of the bounding box as foreground.
Not really sure what you want to do, but you can use Haar Classifier for face detection.
From then on, it should be easy to only display the face. While there are available classifiers online, you can try training your own classifier should you have the time. I have done classifiers on hand, face, eyes before and it have an impressive result.
Should you need more help on training classifier, etc, just comment here, I will try my best to assist you.
Face detection functionality is also available in the Computer Vision System Toolbox for MATLAB in the form of the vision.CascadeObjectDetector object.
I have been working around with OpenCV for few days now and I have a project where I should detect cars and humans from the sky.
So here are my inputs:
A moving camera in the sky (embedded on a quadcopter) which is gonna capture frames.
A set of objects I should detect (humans and cars)
And here are my output:
A detection of those objects outlined by a rectangle or some contours
Based on that, my question is as follows: Which one between Haar Cascade and Hog Detection would you recommend to do so and why? Or any else?
Many thanks for your answers
HOG is usually better for human detection, than Haar. I have only experience in this so I thought I'd give some input on that. However, the limitation of HOG is that the human must be within a "perfect" area on the screen. Too close, it won't detect the human. Too far, it won't detect the human.
I have had better luck with HOG than Haar. Haar gave me too many false positives.
I have been trying to use HAAR to detect human, and it turns out to give too many false positives. I think HAAR is only suitable for face or eye detection.
since your camera is in the sky, the human is pretty small in the image and got a whole body shape. HOG would be a better choice.
You need to change scale factor and minimum neighbours in HAAR cascade which is not same for all the image. So it's better to use HOG.
I'm working on an application which needs to detect the location of a face in a video stream, using a web cam placed at desk height (and slightly off to the side of the user).
I've already implemented a version of OpenCV (using their Haar detection) and it works ok... the problem is that it tends to lose the position of the face if the user turns their head to the side (or looks up).
Since the webcam is sitting on the desk, it is tilted up at a 30 degree angle. The OpenCV detection algorithm is trained using fully frontal images, but not up-angle images like the ones I'm using. I know OpenCV also has a profile Haar file that can be used.. but from my research it seems that the results are quite mixed on profile detection. In addition, I don't really have control over the background or lighting of the image... so this sometimes also effects the efficacy of the OpenCV detection algorithm.
So, I guess what I'm asking is... are there other face detection algorithms (that are hopefully free, as this is part of my university research) that are better for detecting faces for this type of setup? It seems like some of the built-in webcams (for Macs and PCs) actually have fairly robust algorithms for detecting faces (and then overlaying cheesy cartoon images over the faces)... but they seem to work well regardless of background or lighting. Do you have any recommendations?
Thanks.
For research purposes, you can use the Haar cascades in OpenCV, things are different if you want to go commercial (in which case you need to consider LBP cascades instead). Just be sure to quote the Viola-Jones paper in your references.
To improve the results of face detection, you have several paths:
individual image detection: you can send rotated images to a frontal cascade to account for some variability without training your own cascade
individual image detection but more work) : train your own cascade in operating conditions closer to the ones of your app
stability in video streams (as in webcams & co.) : this is achieved by adding a layer of tracking around the face detection. Depending on your knowledge about this topic, you can use your own filter, have fun with OpenCV's particle or Kalman filter, implement a simple first or second order low pass filter on the face position or a PID tracker on the detected face...
Any of these tracking filters will enhance a lot your results when processing video streams.
Use CLM-framework for accurate realtime face detection and face landmark detection.
Example of the system in action: http://youtu.be/V7rV0uy7heQ
You may find it useful.