Gender score is the cognitive judgement of the degree of masculinity or femininity of a face which is considered to be a continuum. Gender scores have long been used in psychological studies to understand the complex psychosocial relationships between people.
Perceptual scores for gender and attractiveness have been employed for quality assessment and planning of cosmetic facial surgery. Various neurological disorders have been linked to the facial structure in general and the facial gender perception in particular. While, subjective gender scoring by human raters has been a tool of choice for psychological studies for many years, the process is both time and resource consuming. This study investigates the geometric features used by the human cognitive system in perceiving the degree of masculinity/femininity of a 3D face. It then proposes a mathematical model that can mimic the human gender perception. The results suggest that the human cognitive system employs a combination of Euclidean and geodesic distances between biologically significant landmarks of the face for gender scoring. It proposes a mathematical model that is able to automatically assign an objective gender score to a 3D face with a correlation of up to 0.895 with the human subjective scores. Biography:- Syed Zulqarnain Gilani Syed Zulqarnain Gilani is a PhD scholar in the Computer Science & Software Engineering Department at UWA. His research topic is 3D Morphometric Face Analysis: Applications to Syndrome Delineation. Mr. Gilani did his MS in Electrical Engineering from the National University of Sciences and Technology (NUST), Pakistan and secured the Presidents Gold Medal. Prior to joining UWA, he was an Assistant Professor in NUST. He is working with a multi-disciplinary team of scientists to find possible alternatives diagnosis of syndromes like Autism from 3D faces. His research interests include computer vision, 3D face analysis, pattern recognition and machine learning. Faisal Shafait Faisal Shafait is working as a Research Assistant Professor in the Computer Science & Software Engineering Department at The University of Western Australia. Formerly, he was a Senior Researcher at the German Research Center for Artificial Intelligence (DFKI), Germany and a visiting researcher at Google, California. He received his Ph.D. in computer engineering with the highest distinction from Kaiserslautern University of Technology, Germany in 2008. His research interests include machine learning and pattern recognition with a special emphasis on applications in document image analysis. He has co-authored over 100 publications in international peer-reviewed conferences and journals in this area. He is an Editorial Board member of the International Journal on Document Analysis and Recognition (IJDAR), and a Program Committee member of leading document analysis conferences including ICDAR, DAS, and ICFHR. He is also serving on the Leadership Board of IAPR’s Technical Committee on Computational Forensics (TC-6). Professor Ajmal Mian Professor Ajmal Mian completed his PhD from The University of Western Australia in 2006 with distinction and received the Australasian Distinguished Doctoral Dissertation Award from Computing Research and Education Association of Australasia. He received the prestigious Australian Postdoctoral and Australian Research Fellowships in 2008 and 2011 respectively. He received the UWA Outstanding Young Investigator Award in 2011 and the West Australian Early Career Scientist of the Year award in 2012. He has secured four Australian Research Councit grants worth over $2 Million. He is currently a Professor in the School of Computer Science and Software Engineering at The University of Western Australia. His research interests include computer vision, 3D shape analysis, pattern recognition, machine learning, multimodal biometrics, and hyperspectral image analysis. PS* This seminar is free and open to the public & no RSVP required. ****All Welcome****
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