Projects

Here you can find information about our current, past, and future projects.

MORPH (Craniofacial Longitudinal Morphological Face Database)


MORPH is the largest publicly available longitudinal face database. It is actively being used in over 30 countries. The MORPH data corpus embraces thousands of facial images of individuals across time, collected in real-world conditions (not a controlled collection). Moreover, these images are available to the public for continued research, and we encourage studies of forensic science relevance and utility. MORPH is comprised of two datasets, or "albums," Album1 and Album2.

Album 1 contains digital scans of 515 photographs of individuals taken between October 26, 1962 and April 7, 1998 which we refer to as acquisition dates. The acquisition dates correspond to increasing ages for individuals in the database; these dates range anywhere from 46 days to 29 years after the earliest photograph. A stats sheet for Album1 is available. Album 2 contains longitudinal digital photographs collected over several years. Album2 is still evolving and images are acquired quarterly.  Both albums include meta data for race, gender, date of birth, and date of acquisition. Eye coordinates for the sets are also available upon request.

Comprising of these albums, we offer three sets of images to the public. Album 1 is available in full as described above. A subset of Album 2 is available for acedemic researchers and contains 55,134 images of 13,000 individuals collected over four years with applicable metadata described above. See the whitepaper for more statistics on this release.

A larger, commercial version of MORPH is also available. The commercial version contains a significantly larger set images from Album 2 collected over a greater time span and includes full meta data with height and weight when available.  Coarse landmark data, eye coordinates, and feature flags will be made available as well. A whitepaper outlining the statistics of this release is available upon request.

MORPH dataset now has a minimal cost to support its development and distribution. However, there are limited coupons available to academic researchers. To receive a coupon the researcher must email (ricanekk AT uncw.edu) from an official academic email address requesting the coupon, stating the purpose of the request. Coupon users will still need to create an account, select the dataset, and go to check-out which is where the coupon will be entered. This link will take you to a different server that requires you to create an account for access to MORPH dataset.

Publications | Request Access

Any work that uses or incorporates the database must acknowledge the authors by including the following reference:

Karl Ricanek Jr and Tamirat Tesafaye, "MORPH: A Longitudinal Image Database of Normal Adult Age-Progression," IEEE 7th International Conference on Automatic Face and Gesture Recognition, Southampton, UK, April 2006, pp 341-345.

We have several data collection efforts underway that will expand our set of databases.  Please see below.

Face Recognition


Iris Recognition We are develop solutions that will enhance the performance of face recognition engines by reducing accuracy errors associated with longitudinal aging.  We have published articles on mitigating aging effects on commercial FR engines. (See pub lists.)  We collaborate with FR developers and manufacturers to assist them in designing a more robust system to aging.  Our long experience in this area has imparted lots of wisdom regarding feature selections and matching enhancements. We have extended the work for adult aging to that of children with support by researchers at Concordia University.  

Most FR work has been evaluated on adult faces, and adult faces close in time (few days to few years between gallery and probe). We will soon begin a large collection effort focused on children and teenagers (pre-adults) to address the concerns of FR efficacy on this group.  We hope to make this data collection available to the research community in the future.  For more information, please contact Dr. Karl Ricanek Jr (ricanekk@uncw.edu, (910) 547-0994).
Publications

Synthetic Face Aging


Photorealistic Age Morphable Face Models Synthetic aging is our bread-and-butter, we have developed several algorithms for synthetically aging and/or de-aging a person.  Our techniques are grounded in the science of craniofacial morphology.  We have notable experts on our team or in collaboration that we leverage to increase our understanding of the mechanisms associated with aging.  Our techniques are built upon this understanding of the fundamental principles of aging for adults and youth.  Therefore, we are able to create realistic synthetic images of future / past faces based on the individual drivers of a person, this is known as idiosyncratic aging.  Most techniques employed today are based upon general aging trends, which applies these aging trends to all persons equally.  This approach cannot account for the differences in aging due genetics, environment and/or behavior. For more information, please contact Dr. Karl Ricanek Jr (ricanekk@uncw.edu, (910) 547-0994).
Publications

Iris Recognition and Perioculur Recognition


Iris Recognition We are investigating the use of low cost iris sensors and their efficacy as a stable verification platform that can be integrated into small appliances. We are also exploring, in concert with our CASIS partners, the ability to perform long range iris, bit-code reduction and mitigation of fragile bits, and fusion of iris with skin region around the eye (periocular).  We are developing models of aging around the periocular and have partnered with a respected plastic surgeon to understand the dramatic changes that occur in this region due to aging and reconstructive surgery. For more information, please contact Dr. Karl Ricanek Jr (ricanekk@uncw.edu, (910) 547-0994).
Publications

Craniofacial Micro / Macro Gestures


We are actively looking for sponsors to explore our ideas in craniofacial micro / macro gestures.  We have some very promising results in this area for identity verification and trustworthyness.  If you are interested in learning more about this work, please contact Dr. Karl Ricanek Jr (ricanekk@uncw.edu, (910) 547-0994)

Age Estimation


We have developed several algorithms for age estimation that are cutting edge.  These algorithms continue to push the performance on standard databases like FG-NET.  We have published results with mean absolute error (MAE) on FG-NET at 4.37 years (with Concordia University).  Our latest results on FG-NET resulted in rates below 4.0 years in cross-validation testing.  We have also developed algorithms that show promise against ethnically diverse data sets.  For more information, please contact Dr. Karl Ricanek Jr (ricanekk@uncw.edu, (910) 547-0994).
Publications

Gender Classification


Gender and Race Classification We have leveraged feature sets from age estimation for gender classification.  We are building unified set of features that can be used for all demographic information extraction.  Gender classification teamed with age estimation is a powerful tool for marketing and access control.  For more information, please contact Dr. Karl Ricanek Jr (ricanekk@uncw.edu, (910) 547-0994).
Publications

Race Classification


Gender and Race Classification Race classification is an extremely difficult topic due to the cross-pollination of racial groups (known as admixture).  However, we are work on developing coarse race classification systems.
Publications

Craniofacial Mophology: The Understanding


The Face Aging Group was born out of the desire to fundamentally understand the mechanisms of aging (craniofacial morphology).  From our inception we have been dedicated to advancing the understanding of how and why the face changes and what are the indicators for future changes (morphology).  Only, by understanding these principles can one develop idiosyncratic aging algorithms.  Also, this knowledge gives us the unique position of understanding why FR breaks and how it may be fixed. For more information, please contact Dr. Karl Ricanek Jr (ricanekk@uncw.edu, (910) 547-0994).
Publications

Mobile Biometrics


Mobile biometrics are defined as the use of biometric techniques on mobile devices. Mobile devices are the perfect platform for many biometric systems as they have very powerful processors, large storage, high resolution cameras, and a small, portable footprint. Mobile biometrics encompass the development of mobile devices to acquire biometric signals, software algorithms for identification and verification, and data stores to house biometric data for comparison. Our current research focus is on the integration of mobile devices with face recognition. We have developed some prototype systems over the last few years to incorporate face recognition on camera phones (circa 2007) and the integration of texting with face recognition. For more information, please contact Dr. Karl Ricanek Jr. (ricanekk@uncw.edu, (910) 547-0994).
Publications