References
- 1DeBruine L. webmorphR: Reproducible Stimuli. Zenodo; 2022. DOI: 10.5281/zenodo.6570965
- 2Gaspar C, Sekuler AB, Bennett PJ. Spatial frequency tuning of upright and inverted face identification. Vision Research. 2008;48(28):2817–2826. DOI: 10.1016/j.visres.2008.09.015
- 3Pachai MV, Sekuler AB, Bennett PJ. Sensitivity to Information Conveyed by Horizontal Contours is Correlated with Face Identification Accuracy. Frontiers in Psychology. 2013;4:
74 . DOI: 10.3389/fpsyg.2013.00074 - 4Smith FW, Schyns PG. Smile through your fear and sadness: transmitting and identifying facial expression signals over a range of viewing distances. Psychological Science. 2009;20(10):1202–1208. DOI: 10.1111/j.1467-9280.2009.02427.x
- 5Yang N, Shafai F, Oruc I. Size determines whether specialized expert processes are engaged for recognition of faces. Journal of Vision. 2014;14(8):
17 . DOI: 10.1167/14.8.17 - 6Hsieh P-J, Vul E, Kanwisher N. Recognition Alters the Spatial Pattern of fMRI Activation in Early Retinotopic Cortex. Journal of Neurophysiology. 2010;103(3):1501–1507. DOI: 10.1152/jn.00812.2009
- 7Petro LS, Smith FW, Schyns PG, Muckli L. Decoding face categories in diagnostic subregions of primary visual cortex. The European Journal of Neuroscience. 2013;37(7):1130–1139. DOI: 10.1111/ejn.12129
- 8Goffaux V, Duecker F, Hausfeld L, Schiltz C, Goebel R. Horizontal tuning for faces originates in high-level Fusiform Face Area. Neuropsychologia. 2016;81:1–11. DOI: 10.1016/j.neuropsychologia.2015.12.004
- 9Iidaka T, Yamashita K, Kashikura K, Yonekura Y. Spatial frequency of visual image modulates neural responses in the temporo-occipital lobe. An investigation with event-related fMRI. Brain Research. Cognitive Brain Research. 2004;18(2):196–204. DOI: 10.1016/j.cogbrainres.2003.10.005
- 10Gold JM, Mundy PJ, Tjan BS. The perception of a face is no more than the sum of its parts. Psychological science. 2012;23(4):427–434. DOI: 10.1177/0956797611427407
- 11Lai J, Pancaroglu R, Oruc I, Barton JJS, Davies-Thompson J. Neuroanatomic correlates of the feature-salience hierarchy in face processing: an fMRI -adaptation study. Neuropsychologia. 2014;53:274–283. DOI: 10.1016/j.neuropsychologia.2013.10.016
- 12Peterson MF, Eckstein MP. Looking just below the eyes is optimal across face recognition tasks. Proceedings of the National Academy of Sciences of the United States of America. 2012;109(48):E3314–3323. DOI: 10.1073/pnas.1214269109
- 13Sekuler AB, Gaspar CM, Gold JM, Bennett PJ. Inversion leads to quantitative, not qualitative, changes in face processing. Current biology: CB. 2004;14(5):391–396. DOI: 10.1016/j.cub.2004.02.028
- 14Vinette C, Gosselin F, Schyns PG. Spatio-temporal dynamics of face recognition in a flash: it’s in the eyes. Cognitive Science. 2004;28(2):289–301. DOI: 10.1016/j.cogsci.2004.01.002
- 15Dakin SC, Watt RJ. Biological “bar codes” in human faces. Journal of Vision. 2009;9(4):
2 .1–10. DOI: 10.1167/9.4.2 - 16Keil MS. “I look in your eyes, honey”: internal face features induce spatial frequency preference for human face processing. PLoS Comput Biol. 2009;5(3):
e1000329 . DOI: 10.1371/journal.pcbi.1000329 - 17Xu X, Biederman I, Shah MP. A neurocomputational account of the face configural effect. Journal of Vision. 2014;14(8):
9 . DOI: 10.1167/14.8.9 - 18King DE. Dlib-ml: A machine learning toolkit. Journal of Machine Learning Research. 2009;10(Jul):1755–1758.
- 19Afchar D, Nozick V, Yamagishi J, Echizen I. MesoNet: a Compact Facial Video Forgery Detection Network. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS); 2018. DOI: 10.1109/WIFS.2018.8630761
- 20Amos B, Ludwiczuk B, Satyanarayanan M. OpenFace: A general-purpose face recognition library with mobile applications; 2016.
- 21Korshunova I, Shi W, Dambre J, Theis L. Fast Face-Swap Using Convolutional Neural Networks. In: 2017 IEEE International Conference on Computer Vision (ICCV).
IEEE ;2017 . DOI: 10.1109/ICCV.2017.397 - 22Liu H, Lu J, Feng J, Zhou J. Group-aware deep feature learning for facial age estimation. Pattern Recognition. 2017;66:82–94. DOI: 10.1016/j.patcog.2016.10.026
- 23Nirkin Y, Masi I, Tran Tuan A, Hassner T, Medioni G. On Face Segmentation, Face Swapping, and Face Perception. In: 2018 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018);
2018 . DOI: 10.1109/FG.2018.00024 - 24Scherhag U, Budhrani D, Gomez-Barrero M, Busch C.
Detecting Morphed Face Images Using Facial Landmarks . In: Image and Signal Processing. Springer International Publishing; 2018. DOI: 10.1007/978-3-319-94211-7_48 - 25Jones AL, Schild C, Jones BC. Facial metrics generated from manually and automatically placed image landmarks are highly correlated. Evolution and Human Behavior. 2021;42(3):186–193. DOI: 10.1016/j.evolhumbehav.2020.09.002
- 26Shen J, Palmeri TJ. The perception of a face can be greater than the sum of its parts. Psychonomic Bulletin & Review. 2015;22(3):710–716. DOI: 10.3758/s13423-014-0726-y
- 27Peterson MF, Abbey CK, Eckstein MP. The surprisingly high human efficiency at learning to recognize faces. Vision Research. 2009;49(3):301–314. DOI: 10.1016/j.visres.2008.10.014
- 28Dakin SC, Watt RJ. Biological “bar codes” in human faces. J Vis. 2009;9(4):
2 .1–10. DOI: 10.1167/9.4.2 - 29Gronenschild EHBM, Smeets F, Vuurman EFPM, Boxtel MPJ van, Jolles J. The use of faces as stimuli in neuroimaging and psychological experiments: a procedure to standardize stimulus features. Behav Res Methods. 2009;41(4):1053–60. DOI: 10.3758/BRM.41.4.1053
- 30Dal Martello MF, Maloney LT. Lateralization of kin recognition signals in the human face. J Vis. 2010;10(8):
9 . DOI: 10.1167/10.8.9 - 31Dupuis-Roy N, Fiset D, Dufresne K, Caplette L, Gosselin F. Real-world interattribute distances lead to inefficient face gender categorization. J Exp Psychol Hum Percept Perform. 2014;40(4):1289–94. DOI: 10.1037/a0037066
- 32Gilad-Gutnick S, Harmatz ES, Tsourides K, Yovel G, Sinha P. Recognizing Facial Slivers. J Cogn Neurosci. 2018;30(7):951–962. DOI: 10.1162/jocn_a_01265
- 33Matthews I, Baker S. Active Appearance Models Revisited. International Journal of Computer Vision. 2004;60(2):135–164. DOI: 10.1023/B:VISI.0000029666.37597.d3
- 34Yu H, Garrod OGB, Schyns PG. Perception-driven facial expression synthesis. Computers & Graphics. 2012;36(3):152–162. DOI: 10.1016/j.cag.2011.12.002
- 35Adams DC, Otárola-Castillo E. geomorph: an R package for the collection and analysis of geometric morphometric shape data. Methods in Ecology and Evolution. 2013;4(4):393–399. DOI: 10.1111/2041-210X.12035
- 36Gao W, Cao B, Shan S, Chen X, Zhou D, Zhang X, Zhao D. The CAS-PEAL large-scale Chinese face database and baseline evaluations. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans. 2008;38(1):149–161. DOI: 10.1109/TSMCA.2007.909557
- 37Burton AM, White D, McNeill A. The Glasgow Face Matching Test. Behavior Research Methods. 2010;42(1):286–291. DOI: 10.3758/BRM.42.1.286
- 38Fysh MC, Bindemann M. The Kent Face Matching Test. British Journal of Psychology. 2018;109(2):219–231. DOI: 10.1111/bjop.12260
- 39Tottenham N, Tanaka JW, Leon AC, McCarry T, Nurse M, Hare TA, Marcus DJ, Westerlund A, Casey B, Nelson C. The NimStim set of facial expressions: Judgments from untrained research participants. Psychiatry research. 2009;168(3):242–249. DOI: 10.1016/j.psychres.2008.05.006
