The role of eye movement consistency in learning to recognise faces: Computational and experimental examinations

[1]  J. Hsiao,et al.  Modulation of mood on eye movement and face recognition performance. , 2020, Emotion.

[2]  Mei Wang,et al.  Deep Face Recognition: A Survey , 2018, Neurocomputing.

[3]  Antoni B. Chan,et al.  Eye-movement patterns in face recognition are associated with cognitive decline in older adults , 2018, Psychonomic bulletin & review.

[4]  Tim Chuk,et al.  Hidden Markov model analysis reveals the advantage of analytic eye movement patterns in face recognition across cultures , 2017, Cognition.

[5]  Jianfei Cai,et al.  Enriched Deep Recurrent Visual Attention Model for Multiple Object Recognition , 2017, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[6]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[7]  Tim Chuk,et al.  Understanding eye movements in face recognition using hidden Markov models. , 2014, Journal of vision.

[8]  Alex Graves,et al.  Recurrent Models of Visual Attention , 2014, NIPS.

[9]  Ha Hong,et al.  Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.

[10]  Matthew F. Peterson,et al.  Individual Differences in Eye Movements During Face Identification Reflect Observer-Specific Optimal Points of Fixation , 2013, Psychological science.

[11]  Reinhold Kliegl,et al.  The generation of secondary saccades without postsaccadic visual feedback. , 2013, Journal of vision.

[12]  Antoni B. Chan,et al.  Clustering hidden Markov models with variational HEM , 2012, J. Mach. Learn. Res..

[13]  Jon Brock,et al.  Visual Scan Paths and Recognition of Facial Identity in Autism Spectrum Disorder and Typical Development , 2012, PloS one.

[14]  S. Jaeggi,et al.  The concurrent validity of the N-back task as a working memory measure , 2010, Memory.

[15]  Janet Hui-wen Hsiao,et al.  NIMBLE: a kernel density model of saccade-based visual memory. , 2008, Journal of vision.

[16]  G. Cottrell,et al.  Two Fixations Suffice in Face Recognition , 2008, Psychological science.

[17]  Benjamin W Tatler,et al.  The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions. , 2007, Journal of vision.

[18]  Frédéric Gosselin,et al.  Spatio-temporal dynamics of face recognition in a flash: it's in the eyes , 2004, Cogn. Sci..

[19]  I. Craw,et al.  Effects of high-pass and low-pass spatial filtering on face identification , 1996, Perception & psychophysics.

[20]  R. Reitan.  Validity of the Trail Making Test as an Indicator of Organic Brain Damage , 1958 .

[21]  AB Chan,et al.  Simplifying mixture models with the hierarchical EM Algorithm, and its application to analyzing eye movement patterns in face recognition , 2018 .

[22]  Howard Riley,et al.  Are Portrait Artists Superior Face Recognizers? Limited Impact of Adult Experience on Face Recognition Ability , 2017, Journal of experimental psychology. Human perception and performance.

[23]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[24]  J. O'Regan,et al.  Some results on translation invariance in the human visual system. , 1990, Spatial vision.

[25]  C. Eriksen,et al.  Effects of noise letters upon the identification of a target letter in a nonsearch task , 1974 .

[26]  F. K. Berrien.  The effects of noise. , 1946, Psychological bulletin.