Event Location: NSH 1507
Bio: Aleix M. Martinez is an Associate Professor in the Department of
Electrical and Computer Engineering at The Ohio State University (OSU),
where he is the founder and director of the Computational Biology and
Cognitive Science Lab. He is also affiliated with the Department of
Biomedical Engineering and to the Center for Cognitive Science. Prior to
joining OSU, he was affiliated with the Electrical and Computer
Engineering Department at Purdue University and with the Sony Computer
Science Lab. He serves as an associate editor of IEEE Transactions on
Pattern Analysis and Machine Intelligence and of Image and Vision
Computing and has been an area chair of CVPR. Aleix has spent his time
wondering why he is such a bad face recognizer and why people attribute
social labels to faces of unknown individuals. His other areas of
interest are learning, vision and linguistics.

Abstract: Faces and emotions shape our daily life in many different ways. We are
so intrigued about such effects that writers, poets and painters have
been depicting and portraying them for centuries. Why does the male
character in Wood’s “American Gothic” seem sad? Why do kids elongate
their faces when they are upset? Why do some people always seem angry?
Why do we recognize identity from faces so easily? Why is it so hard to
learn non-manuals (i.e., facial expressions of grammar) in sign
languages, when native signers do this effortlessly? In short, what are
the dimensions of our computational (cognitive) space responsible for
these face processing tasks? If we are to understand why things appear
as they do and how cognitive disorders in Autism, schizophrenia and
Huntington’s disease develop, we need to define how the brain codes and
interpreters faces, emotions and grammar. This is also important for the
design of technology – as devices need to interact with us. In this
talk, I will outline the research framework I use to study face
perception and the related topics of emotion and grammar. This consists
of a multidisciplinary approach in cognitive science, including
psychophysical studies and the design of computer algorithms for the
analysis of face images. We will review the big questions about this
computational space. In doing so, we will see that the ability of human
observers to process face images is truly remarkable, suggesting that
some abstract, yet simple representation that is unaffected by a large
number of image transformations is at work. We will summarize our
current understanding of this representation.