I am a Full Professor in the Electrical and Computer Engineering Department of Carnegie Mellon University and also in Carnegie Mellon's CyLab. My research is in developing Biometric Identification technologies and algorithms that work under co-operative scenarios (, i.e. recognizing a person based on their face, iris, fingerprint,and palmprints) and also un-cooperative scenarios (using surveillance data to recognize a person). I collaborate and work in joint projects with Prof. B.V.K. Vijaya Kumar and Prof. Pradeep Khosla.

My research in Biometrics has been mostly focused on Face Recognition and Iris Recognition, developing new technology that can achieve distortion tolerant face & iris recognition. The appearance of face images can vary due to a number of factors such as pose, expression and illumination. Thus I have been researching in developing techniques such as advanced correlation filters that have built-in tolerance to such variations. In the iris field, intra-class variations include local deformations, focus blur and off-angle iris views.

Recently (the past year) I have been spearheading and leading our CMU efforts in the Face Recognition Grand Challenge (FRGC) and the Iris Challenge Evaluation(ICE) which are parts of NIST's efforts in evaluating and identifying key performance technologies in Face recognition and Iris Recognition.This is a project that I work jointly with Prof. B.V.K. Vijaya Kumar, infact we are the only two faculty in CMU participating in FRGC and FRVT (the Face Recognition Vendor Test 2006) and more remarkeably we are also participating in ICE too (that makes us the only group doing both in academia and industry!).

In the latest FRGC-Phase II, I have been involved and leading the development of our novel recognition approach for tackling Exp 4 in FRGC-II data, yielding 72% verification @ 0.1% False Acceptance Rate(FAR) in the pure 1-1 FRGC matching protocol (these is based using all pairwise comparisons of target/query images on the FRGC dataset). This is a dramatic improvement in performance of the baseline PCA algorithm which yeilds only 12% verification @ 0.1 % FAR. Our algorithm ranked 2nd with the top being at 76% @ 0.1% FAR. However, in a practical scenario, we have more than one mug-shot of a suspect that we are looking for, when we use such information and train using all images of a person in the target set, we can boost performance in FRGC Exp4 to 92% @ 0.1% FAR using discriminant learning with support vector machines and our advanced correlation feature extraction method.

In the latest ICE-Phase I results (March 22, 2006), we have performed extremely well, yielding 99.63% @ 0.1% FAR for Experiment 1, putting us in 2nd rank for all ICE participants from Industry (only 1 commercial product was able to outperform our algorithm) and we were in #1 rank compared to other academic participants.

My general research interests include:- Face recognition/ detection, Iris Segmentation and Recognition, other biometrics (such as palmprint and fingerprint), steganography/data hiding, encrypting biometrics and performing recognition in the encrypted domain, wavelet and Fourier analysis for pattern recognition and representation, Digital signal & image processing, pattern recognition, SupportVector Machines, Neural Networks, discriminative subspace modelling, advanced correlation filters and kernel methods.