Strategies underlying face and object recognition
In a series of studies, we have investigated the nature of information used in ecologically
important tasks of object recognition. Of primary interest to us is the recognition of faces
when image quality is degraded due to refractive errors, amblyopia or large viewing distances.
We have estimated lower-bounds on the image resolution needed for face detection and identification,
and titrated the contribution of cues such as pigmentation and configuration. The resulting data have not
only provided clues regarding the nature of representations the brain uses for achieving its robust performance,
but have also served as starting points for the creation of robust artificial vision systems.
Sinha, P., Balas, B. J., Ostrovsky, Y. and Russell, R. (2005).
Face recognition by humans. In Face Recognition:
Advanced Modeling and Methods, Academic Press.
Russell, R., Sinha, P., Biederman, I., & Nederhouser, M. (2007).
Is pigmentation important for face recognition? Evidence from contrast negation. Perception.
Riesenhuber, M., Jarudi, I., Gilad, S. and Sinha, P. (2004).
Face processing in humans is compatible with a simple shape-based model of vision..
Proceedings of the Royal Society of London, B. (Suppl.), 04BL0061.S1-S3.
Sinha, P. (2003). The use of 2-D similarity metrics for 3-D object recognition.
IETE Journal of Research, Vol. 49, Nos. 2 and 3, pp 113-125.
Yip, A. and Sinha, P. (2002).
Role of color in face recognition. Perception,
Vol. 31, pp 995-1003
Sinha, P. (2002). Recognizing
complex patterns. Nature Neuroscience, Vol. 5 (suppl.),
Sinha, P. & Poggio, T. (1996).
I think I know that face...,
Nature, Vol. 384, No. 6608, pp. 404.
Vocabulary of recognition
We have complemented our experimental investigations of the nature of information used for
various recognition tasks, with theoretical proposals for how such information can be formally represented.
We have developed two related representation schemes that encode images in such a way as to
attain a significant measure of tolerance to changes caused by illumination variations, resolution reduction and noise.
The approaches are biologically plausible and, in computational simulations, are found to improve recognition
performance over that obtained with conventional strategies such as those involving Gabor wavelets.
Furthermore, the schemes suggest interesting new ways of conceptualizing the transforms effected by
early visual areas in the mammalian brain, and have begun guiding neuro-physiological investigations of novels
kinds of receptive-field structures in the primate visual pathway.
Balas, B. J. and Sinha, P. (2006).
Receptive field structures for recognition. Neural Computation.
Sadr, J., Mukherjee, S., Thoresz, K. & Sinha, P. (2002).
The Fidelity of Local Ordinal Encoding. In T. Dietterich, S. Becker & Z. Ghahramani (Eds.),
Advances in Neural Information Processing Systems 14. MIT Press: Cambridge, MA.
Sinha, P. (2002).
Qualitative representations for recognition. In Lecture Notes in
Computer Science, Springer-Verlag, LNCS 2525, pp 249-262.
Oren. M., Papageorgiou, C., Sinha, P., Osuna, E. and Poggio,
T. (1997) Pedestrian detection using wavelet templates. Proceedings
of the IEEE Computer Society Conference on Computer Vision and
Pattern Recognition, San Juan, Puerto Rico.
Neural correlates of recognition
In conjunction with our behavioral studies of face perception with degraded images,
we have attempted to determine the neural correlates of this task. Using fMRI, we have
investigated neuronal responses when human observers view clear and highly degraded images of faces.
We find that a specific brain region (in the fusiform gyrus) is activated when participants view clear facial images.
This is not surprising, and several past studies have reported this result. What is surprising, however, is that we
find that this region is also activated even when the images are so degraded that the intrinsic facial information
(the pattern of eyes, nose and mouth) is entirely obliterated, so long as the surrounding contextual cues
(such as the presence of a body) suggest that the degraded region might be a face. In other words,
the neural circuitry in the human brain is such as to be able to use context to compensate for extreme levels
of image degradations.
Besides helping to elucidate the functional architecture of the brain's recognition machinery, these investigations
are also informing our work on neurodevelopmental disorders which are characterized by difficulties in information integration.
Cox, D., Meyers, E. and Sinha, P. (2004).
Contextually evoked object-specific responses in
human visual cortex. Science, Vol. 303, No. 5667, pp 115-117.
Roy, J., Sinha, P. and Miller, E. (2004).
Responses of dorsolateral prefrontal cortex neurons in the monkey to blurred and rotated face images.
Society for Neuroscience Abstracts.
Vaina, L. M., Solomon, J., Chowdry, S., Sinha, P., Belliveau, J., W., Gross,
C. G. (2001). Functional neuroanatomy of biological motion perception in humans.
Proceedings of the National Academy of Sciences 98, 11656-11661.
Clinical aspects of recognition
We have developed a technique called 'Random Image Structure Evolution' (RISE)
that provides a convenient way of assessing various aspects of high-level visual function.
We have used this technique for tasks as diverse as examining neural correlates of object perception,
on the one hand, and probing recognition-skill differences between children with normal and abnormal
development, on the other.
Sadr, J. & Sinha, P. (2004).
Object Recognition and Random Image Structure
Evolution . Cognitive Science, Vol. 28, pp 259-287.
Pollak, S. and Sinha, P. (2002).
Enhanced perceptual sensitivity for anger
among physically abused children. Developmental Psychology,
Vol. 38, No. 5, pp 784-791.