I'm developing an app which uses Night Vision camera to recognize the face features even under low light conditions. My app is made to work at night, I've no problems working with camera in day light, to make it work in low light I want to use night vision camera (Please suggest me any good night vision camera for Processing using OpenCV). Or is there any way to get better quality image under low light even from the normal camera.
Most digital cameras are able to see infrared light. You can easily verify this by pointing a tv remote at a camera while pressing buttons.
Shining ir leds to illuminate the scene is pretty common in surveillance just look at this camera http://thespystore.com/image/cache/data/weatherproof-night-vision-camera-bulir11-500x500.JPG. Usually you can't use color for night vision. It's the same as having a grayscale image. Good luck
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Does anyone know how to reproduce the new Notes new scanning feature in iOS 11??
Is AVFoundation used for the camera?
How is the camera detecting the shape of the paper/document/card?
How do they place the overlay over in real time?
How does the camera know when to take the photo?
What's that animated overlay and how can we achieve this?
Does anyone know how to reproduce this?
Not exactly :P
Is AVFoundation used for the camera? Yes
How is the camera detecting the shape of the paper/document/card?
They are using the Vision Framework to do rectangle detection.
It's stated in this WWDC session by one of the demonstrators
How do they place the overlay over in real time?
You Should check out the above video for this as he talks about doing something similar in one of the demos
How does the camera know when to take the photo?
I'm not familiar with this app but it's surely triggered in the capture session, no?
Whats that animated overlay and how can we achieve this?
Not sure about this but I'd imagine it's some kind of CALayer with animation
Is Tesseract framework used for the image afterwards?
Isn't Tesseract OCR for text?
If you're looking for handwriting recognition, you might want to look for a MNIST model
Use Apple’s rectangle detection SDK, which provides an easy-to-use API that can identify rectangles in still images or video sequences in near-realtime. The algorithm works very well in simple scenes with a single prominent rectangle in a clean background, but is less accurate in more complicated scenes, such as capturing small receipts or business cards in cluttered backgrounds, which are essential use-cases for our scanning feature.
An image processor that identifies notable features (such as faces and barcodes) in a still image or video.
https://developer.apple.com/documentation/coreimage/cidetector
I planned to develop a software that can takes attendant (work , school) by face recognition as my final year project(FYP).(Just an Idea)
I have search through the net about the image processing library and i found out OpenCv is more well known as i found a lot of video for face recognition using OpenCv in youtube which will definitely help me a lot.(I'm totally new to image processing). Also, i will be using Visual Studio.
Here come the first problem, which is is it possible to detect that it is a photo or a real person is standing in front of the camera while taking the attending?
If yes, can you provide me some link or tutorial link for how image processing can detect 'photograph' and 'real person'?
As i said, I'm totally new to image processing and this is just an idea for my FYP
Or is the any open sources library that you recommend?
Eulerian Video Magnification can detect that it is a photo or a real person is standing in front of the camera but it may not detect that it is a video or real person is standing in front of the camera. Thus, the Face Recognition Authentication System which is based Eulerian Video Magnification can not be successfull when malicious user uses face video to rather than a real person face.
Here are my ideas to develop robust Face Recogition Authentication system;
You can use Multi-View Face Recognition to develop robust face authentication system. Here is a demo video of this technique and here are papers to get theoritical background. Also you can benefit from this, this, this and this when you start coding.
You can use RANDOM directions to detect that it is a photo/video or real person for example blink your eyes 3 times, move your eyebrow, look at the left side or look at the right side (multi-view face recognition will be used to recognize user's face when user look at the right or left) etc.
You should use these 2 ideas in your project for developing robust Face
Recognition Authentication system.
Here is the scnario;
I am working on an app in iOS that will occur an event if camera detects some changes in image or we can say motion in image. Here I am not asking about face recognition or a particular colored image motion, And I got all result for OpenCV when I searched, And I also found that we can achieve this by using gyroscope and accelerometer both , but how??
I am beginner in iOS.So my question is , Is there any framework or any easy way to detect motion or motion sensing by camera.And How to achieve?
For Example if I move my hand before camera then it will show some message or alert.
And plz give me some useful and easy to understand links about this.
Thanx
If all you want is some kind of crude motion detection, my open source GPUImage framework has a GPUImageMotionDetector within it.
This admittedly simple motion detector does frame-to-frame comparisons, based on a low-pass filter, and can identify the number of pixels that have changed between frames and the centroid of the changed area. It operates on live video and I know some people who've used it for motion activation of functions in their iOS applications.
Because it relies on pixel differences and not optical flow or feature matching, it can be prone to false positives and can't track discrete objects as they move in a frame. However, if all you need is basic motion sensing, this is pretty easy to drop into your application. Look at the FilterShowcase example to see how it works in practice.
I don't exactly understand what you mean here:
Here I am not asking about face recognition or a particular colored
image motion, because I got all result for OpenCV when I searched
But I would suggest to go for opencv as you can use opencv in IOS. Here is a good link which helps you to setup opencv in ios.
There are lot of opencv motion detection codes online and here is one among them, which you can make use of.
You need to convert the UIImage ( image type in IOS ) to cv::Mat or IplImage and pass it to the opencv algorithms. You can convert using this link or this.
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.
I want to develop an iPhone app which should recognise the eyes, face, and skin color of a person in an image which is scanned by QR Reader.
How can eyes be detected in an image?
Although it may be possible, I'm just warning you that it will have, regardless of the programming, a degree of inaccuracy. Any face/retina detection software is able to be tricked, and considering the quality of the iPhone's camera, it can't capture enough detail to accurately evaluate the geometric relevancy between two retinas. Also, about recognizing the face color, it might be problematic due to varying light conditions. Under florescent lights, the person's skin tone would appear more blue than under incandescent or natural lighting. Maybe there is another way to go about this?
For localizing the eyes I've used the algorithm described in "Accurate Eye Center Location and Tracking Using Isophote Curvature" by Roberto Valenti, Theo Gevers in my master thesis and achieved very good results with it:
http://www.science.uva.nl/research/publications/2008/ValentiCVPR2008/CVPR%2008.pdf
For face detection / localization, use the Viola-Jones algorithm, there is probably a objective-c implementation out there somewhere. (OpenCV has it, alternatively)