Recognizing objects from large image databases, histogram based methods have proved simplicity and usefulness in last decade. Initially, this idea was based on color histograms that were launched by swain . This algorithm presents the first part of our proposed technique named as “Histogram processed Face Recognition” 
For training, grayscale images with 256 gray levels are used. Firstly, frequency of every gray-level is computed and stored in vectors for further processing. Secondly, mean of consecutive nine frequencies from the stored vectors is calculated and are stored in another vectors for later use in testing phase.
This mean vector is used for calculating the absolute differences among the mean of trained images and the test image. Finally the minimum difference found identifies the matched class with test image.
Recognition accuracy is of 99.75% (only one mis-match i.e. recognition fails on image number 4 of subject 17)
 M. J. Swain and D. H. Ballard, “Indexing via color histogram”, In Proceedings of third international conference on Computer Vision (ICCV), pages 390–393, Osaka, Japan, 1990.
 Fazl-e-Basit, Younus Javed and Usman Qayyum, "Face Recognition using processed histogram and phase only correlation ", 3rd IEEE International Conference on Emerging Technology pp. 238-242
is this code for face recognition using histogram matching method?
it's really usefull for me.
but I have question Sir, about your coding.
1. why bin_number must 9 ??
2. why form_bin_number 29?
can you explain the reason?may I change this number?
sir i need some thesis about this code i am doing my final year project on this
Thank,It's really useful with me
i have error in nargin can some one help me plz
It's really useful ,but i need this program to find the matching score between the images.
The first point which you asked for is absolutely right. As the ORL database contains 10 images for each subject and the odd or even contains the maximum pose required to train the system
The second point is also correct that this is simple technique of histogram (gray-level ) matching so it can be fooled but fusing this faster technique to other powerful methods will improve the system (i.e POC etc).
The code provides the simple system of matching where gray-lelvel information is essential
I tested the system on the ORL database (first 5 faces for training and the remaining ones for testing). I obtain 90% identification rate.
I tested the system on the AR database (segmented faces) (faces 1..3 for training and faces 7..9 for testing). I obtain 30% identification rate.
In my opinion this method is not recognizing the face. It is recognizing the shirt, hair, etc. If you remove this information, the method fails.