About Me

I am currently working on my Ph.D. in Computer Science at Michigan State University, while being advised by University Distinguished Professor Anil K. Jain.  My research areas are pattern recognition, computer vision, and machine learning, with an emphasis biometrics and automated video surveillance. I am originally from Northern Virginia. I served as an Army Ranger in 3rd Ranger Battallion, 75th Ranger Regiment. I am currently engaged to be married in the summer of 2010. My personal interests and activities include running (the 2009 Chicago marathon will be my fourth marathon), live music, watching sports, craft beers, and animals.

What is Pattern Recogntion?

Pattern recogntion is the categorization or seperation of signals and data. As humans we do this on a constant basis: from recognizing a friend to distinguishing between a road and a field. While we do not give much thought to these tasks, programming a computer to perform the same feats is far from trivial. Pattern recognition research is most often conducted in computer science departments these days, though it is the mairrage of signal processing (electrical engineering), statististics, linear algebra (math), algrotihmic computer science, and other fields.

The signal processing aspect of pattern recognition typically involves the initial processing of a signal (pattern sensing). One common signal used in pattern recogntion is digital images. Extracting information from these images is known as computer vision and has evolved from signal processing. Signal processing and computer vision algorithms seek to compensate for noise (inaccurate pixel intensities) in images, reduce the dimmensionality of a signal by projecting it into a alternate basis (fourier transform, cosine transform, principal componenet analysis, feature extraction etc.), and extract image features for organization and classification.

The statiscal aspect of pattern recogntion generally consists of a learning phase and a decision phase. These algorithms are also part of the field of machine learning. The learning phase (generally) uses statistical methods learn a model of the problem being addressed. The decision phase then applies this model to a newly aquired signal. Classically machine learning is dichotamized into supervised and unsupervised learning. Supervised learning consits of patterns (extracted during the signal processing phase) that have a known category or label. The key to the learning phase is that data (or observations) are needed, which can be highly expensive to collect.

What are Biometrics?

Biometrics are methods of using characteristics of the human body for the purpose of identification and verification. Typically this involves measurements or assesements of specific parts of the body (single biometrics), though this can also involve the use of multiple regions of the body (multi-biometrics). The three most popular biometrics are fingerprint, face, and iris. Other biometrics that have been used successfully include, and are not limited to, hand, palm, ear, vascular, speech, gait, and dental. Biometrics are an applied field of pattern recogntion, where the sensed data is generally digital images (e.g. scanned fingerprints, face photographs, infared iris images) and the algorithms used generally measure the distance of subjects in a some geometrical space (created from feature-based representations, linear subspaces, etc.).