Digikam/GSoC2012/FaceRecognition

OpenTLD based Appoach

Libkface is facedetection and recognition library.Current implementation Face-recognition implementation used is traditional Eigenface based minimum distace approach.Now after OpenTLD a generic object-detection and tracking algorithm is gaining popularity which has been modified to recognise faces.OpenTLD uses many types of features and concepts including haar- wavelets,Local binary Patterns,Intensity normalissed patches which makes it robust against intensity,orientation aspectratio variations.

Using SQLite instead XMLs for storing data Face Recognition process requires to store and retrieve data as an when needed,so sqlite is good choice to store data.Data used by OpenTLD based face-recognition(discussed in subsequent paragraphs) method can be subdivided into basic datatypes(int,float).There will be a dynamic generation of data which is stored using QList container and are serialised to QBytearray and QString to store in sqlite database.A table containing ID(unique for one facemodel),Name and serialised modeldata is maintaned in QSqlDatabase.

different way of merging multiple faces together. Currently, in the interests of saving space, as new faces are added to a particular person, rather than keeping them all separate they are merged together using eigen projections.It is good because it does save space, loading time and memory in general it tends to lose precision. So a different method of merging would great addition, or perhaps an altogether different approach.

New algorithm for face recognition. Currently Eigenfaces is the working algorithm, however it is coloury and rotation variant. This obviously has an adverse effect on quality of recognition. Fisherfaces is much more tollerant to rotation and colour changes. Hidden Markov Model based approach is another possible way of doing it. There used to be good helper function in OpenCV, however they reside in legacy headers and sadly a lot of documentation has been lost so revere engineering will be required from source. Ultimately an entirely new approach would be equally interesting to consider e.g. recognition based neural maps(network). Ideally the projectis not to invent a new method, but rather implement already existing one from the literature.

Eigenface Improvement in Libface

It was planned that, the eigenface improvement will be done using different distance measures instead of Euclidean distance. But, the problem is in libface, opencv function is currently being used for eigenface calculation. So, the distance measure is also in the opencv code. Improvement this way necessitates the change in opencv code which is impossible or create a new eigenface calculation code of our own which seems distracting from the main purpose of the project.

The support has already been given in the code and also been committed to the svn head. Currently, PGM support of Qt is being used. We may also have used PGM type support from opencv in our code.

OpenTLD Implementation

Merits of OpenTLD over other algorithms. Eigenface recognition is suitable or works for large size biometric images(faces) and fails with intensity and size variations. HMM is suppose to be best algorithm but implementation is difficult and computationally expensive if proper optimizations are not considered.And partial implementation is done.

OpenTLD being realtime object tracking algorithm is expected to be fast and recognition accuracy is good.Moreover it do not need all the images of aperticular person to recognise,but it needs stored facemodel,which is then correlated with the current image and is used to get recognition,so stored model is necessary and sufficient.If a new face of person who already has entry in databse is fed to the system and asked to recognise,it computes tracker and detector fusehypothesis to determine the whether it matches any entry in database,if not user has to tag with the name which is then stored in the database.More about OpenTLD can be obtained from : http://gnebehay.github.com/OpenTLD/

...............currently this implementation is in progress for development and testing and latest code is available in https://github.com/maheshmhegade/NewFaceRecognition