Current Projects
- Simultaneous super-resolution and feature extraction for low-resolution face recognition. Face recognition performance degrades when face images are of very low resolution
since many details about the difference between one person and another can only be captured in images of sufficient resolution. In this work, we propose a new procedure for
recognition of low-resolution probe face images, when there is a high-resolution training
set available.
- Automatic clinical diognosis of otitis media. Otitis media is a general term for middle-ear inflammation and may be classified clinically as either acute otitis media (AOM) or otitis
media with effusion (OME); this clinical classification is important because antibiotics
are generally beneficial only for AOM. However, proper diagnosis of AOM, as well as
distinction from both OME and no effusion (the state of a normal ear) require considerable training.
AOM is the most common infection for which antimicrobial agents are prescribed in
children in the US. In this work we are implementing an automatic recognition system that will classify middle-ear images as obtained by a digital otoscope into the three possible diagnosis.
Past Projects
- Palmprint recognition using advanced correlation filter classifies.
- Adaptive multiresolution transformations for biometric recognition.
- Steganography of biometric modalities.
- Auditory modeling for automatic speech recognition.
- Multiresolution feature extraction for robust speech recognition.