View-Based Recognition of Faces in Man and Machine: Re-Visiting Inter-Extra-Ortho

For humans, faces are highly overlearned stimuli, which are encountered in everyday life in all kinds of poses and views. Using psychophysics we investigated the effects of viewpoint on human face recognition. The experimental paradigm is modeled after the inter-extra-ortho experiment using unfamiliar objects by Bulthoff and Edelman [5]. Our results show a strong viewpoint effect for face recognition, which replicates the earlier findings and provides important insights into the biological plausibility of view-based recognition approaches (alignment of a 3D model, linear combination of 2D views and view-interpolation). We then compared human recognition performance to a novel computational view-based approach [29] and discuss improvements of view-based algorithms using local part-based information.

[1]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[2]  H. Bülthoff,et al.  Face recognition under varying poses: The role of texture and shape , 1996, Vision Research.

[3]  Thomas Vetter,et al.  A morphable model for the synthesis of 3D faces , 1999, SIGGRAPH.

[4]  P. Schyns,et al.  Categorization creates functional features , 1997 .

[5]  H. Abdi,et al.  From Rotation to Disfiguration: Testing a Dual-Strategy Model for Recognition of Faces across View Angles , 1999, Perception.

[6]  H H Bülthoff,et al.  Psychophysical support for a two-dimensional view interpolation theory of object recognition. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[7]  David G. Lowe,et al.  Three-Dimensional Object Recognition from Single Two-Dimensional Images , 1987, Artif. Intell..

[8]  V Bruce,et al.  Configural Features in the Context of Upright and Inverted Faces , 2001, Perception.

[9]  Shimon Ullman,et al.  Object Classification Using a Fragment-Based Representation , 2000, Biologically Motivated Computer Vision.

[10]  David G. Lowe,et al.  Perceptual Organization and Visual Recognition , 2012 .

[11]  Mark H. Johnson,et al.  CONSPEC and CONLERN: a two-process theory of infant face recognition. , 1991, Psychological review.

[12]  J Sergent.  Influence of task and input factors on hemispheric involvement in face processing. , 1985, Journal of experimental psychology. Human perception and performance.

[13]  Heinrich H. Bülthoff,et al.  View-based recognition under illumination changes using local features , 2001, CVPR 2001.

[14]  Heinrich H. Bülthoff,et al.  Acquiring Robust Representations for Recognition from Image Sequences , 2001, DAGM-Symposium.

[15]  Ronen Basri,et al.  Recognition by Linear Combinations of Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  I. Biederman,et al.  Recognizing depth-rotated objects: Evidence and conditions for three-dimensional viewpoint invariance. , 1993 .

[17]  G. Hole,et al.  Featural and Configurational Processes in the Recognition of Faces of Different Familiarity , 2000, Perception.

[18]  Nathan Intrator,et al.  A Productive, Systematic Framework for the Representation of Visual Structure , 2000, NIPS.

[19]  I. Biederman,et al.  Dynamic binding in a neural network for shape recognition. , 1992, Psychological review.

[20]  T. Poggio,et al.  A network that learns to recognize three-dimensional objects , 1990, Nature.

[21]  Maurizio Pilu,et al.  A direct method for stereo correspondence based on singular value decomposition , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  C. C. Goren,et al.  Visual following and pattern discrimination of face-like stimuli by newborn infants. , 1975, Pediatrics.

[23]  A. O'Toole,et al.  Stimulus-specific effects in face recognition over changes in viewpoint , 1998, Vision Research.

[24]  S. Edelman,et al.  Orientation dependence in the recognition of familiar and novel views of three-dimensional objects , 1992, Vision Research.

[25]  Pietro Perona,et al.  Unsupervised Learning of Models for Recognition , 2000, ECCV.

[26]  I. Biederman.  Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[27]  Refractor.  Vision , 2000, The Lancet.

[28]  Heinrich H. Bülthoff,et al.  Automatic acquisition of exemplar-based representations for recognition from image sequences , 2001, CVPR 2001.

[29]  H. Bülthoff,et al.  Effects of temporal association on recognition memory , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Thomas Serre,et al.  Categorization by Learning and Combining Object Parts , 2001, NIPS.

[31]  I Biederman,et al.  Neurocomputational bases of object and face recognition. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.