Soaptechie's profile - activity

Hi All,

I have a code snippet which uses OpenCV's C interface.
The code uses IplImage* and obtains its imageData, widthStep and depth.
Now, We are modifying our code to use C++ based interface and Mat class.
Could you please tell me how to obtain imageData, widthStep and depth from a Mat object?

Thanks,
Soaptechie

Thank you Steven.

Your 2 cents were really helpful !

Removed eye detection part from my application code and to compensate face alignment, now using more faces per person (50 faces per person) for training data to LBPH Face Recognizer. Getting better results compared to my previous attempts.

I appreciate your help.

Helllo All,

I am trying to make Face Recognition work on my Debian Linux. I have done some modification on top of the code developed by Shervin Emami.
(Shervin's code : https://github.com/MasteringOpenCV/code/tree/master/Chapter8_FaceRecognition)
I used LBPH for face recognition as recommended by Berak.
Now, coming to my problems.
(1). It seems that Shervin's approach relies quite a lot on Eye detection so that captured face can be aligned properly. Is this the only way for face alignment? I observed that eye detection takes more time especially when someone is wearing glasses.
(2). Do haarcascade_lefteye_2splits.xml/haarcascade_righteye_2splits.xml detect eyes with glasses?

OpenCV data folder contains several classifiers. Are there any more classifiers for better face/eye detection?

One more thing, Android Face Unlock and Key Lemon's Face Recognition are very fast in terms of face recognition while they don't need more training data.
Any idea which Algorithm is used there?

Thanks,
Soaptechie

It seems setting threshold around 50.0 gives me better results. BTW, Had one question. I looked into LBPH::predict method of OpenCV. From the code, it seems that confidence is minDist so shouldn't it be other way round. I mean lower confidence is better.

Actully, I have faces of 4 persons (15 samples per person). Yes I understand that eigen or fisher requires much more data. Philipp mentioned that eigen/fisher operate on high-dimensional image space so I am kind of avoiding them if possible.

Since I am trying with LBPH, I guess 15 images would be enough (Not sure though). I am following code of GitHub (https://github.com/MasteringOpenCV/code/tree/master/Chapter8_FaceRecognition) written by Shervin.

I did normalization and equalizeHist similar to that code and Model threshold has been set to 100.0 as below

model->set("threshold", 100.0);

With these I got success in prediction upto 70% with confidence values around 45 to 70. I will try out by changing some more parameters. Hopefully get some better results.

Thanks,

Soaptechie

Thank you Berak

I have been following face recognition tutorials written by Philipp Wagner and Shervin Emami. As you suggested about retraining, I am using original images instead of yml/xml file.
I am experimenting with LBPH FaceRecognizer. I found it interesting. Around 70% times, it predicts correct(I have faces of 4 persons for comparison) but I would like the results to converge more than 90-95%. Probably, I will have to to tune some parameters like threshold, radius etc.

Any clue in that direction?

What does confidence mean? Higher confidence is better or lower?

Thanks,

Soaptechie

Thank you Berak

I have been following face recognition tutorials written by Philipp Wagner and Shervin Emami. As you suggested about retraining, I am using original images instead of yml/xml file.
I am experimenting with LBPH FaceRecognizer. I found it interesting. Around 70% times, it predicts correct(I have faces of 4 persons for comparison) but I would like the results to converge more than 90-95%. Probably, I will have to to tune some parameters like threshold, radius etc.

Any clue in that direction?

What does confidence mean? Higher confidence is better or lower?

Thanks,

Soaptechie

Hello All,

I am trying to develop a small application for FaceRecognition in the lines of Android's FaceUnlock. There are two parts of the application.

(1). A model_creator would read the faces to create a model and train it.The model would be saved to a file (say Faces.db).

(2). A predictor would load the same file (Faces.db) and try to make prediction from incoming face captures.

The mode_creator and predictor can be invoked when needed but they both do not run at the same time.

For EigenFaceRecognizer, I would like to add more faces for an existing person or a new person to the model. I understand from the documentation that EigenFaceRecognizer and FisherFaceRecognizer do not support update() method.

How could the model be provided more faces?

Thanks,

Soaptechie