Abstract
We test the possibility of tapping the subconscious mind for face recognition using consumer-grade BCIs. To this end, we performed an experiment whereby subjects were presented with photographs of famous persons with the expectation that about 20% of them would be (consciously) recognized; and since the photos are of famous persons, we expected that subjects would have seen before some of the 80% they didn’t (consciously) recognize. Further, we expected that their subconscious would have recognized some of those in the 80% pool that they had seen before. An exit questionnaire and a set of criteria allowed us to label recognitions as conscious, false, no recognitions, or subconscious recognitions. We analyzed a number of event related potentials training and testing a support vector machine. We found that our method is capable of differentiating between no recognitions and subconscious recognitions with promising accuracy levels, suggesting that tapping the subconscious mind for face recognition is feasible.
- D. Akhawe and A. Porter Felt. 2013. Alice in Warningland: A large-scale field study of browser security warning effectiveness. In 22nd USENIX Security Symposium. Google ScholarDigital Library
- S. Bentin and L. Deouell. 2000. Structural encoding and identification in face processing: ERP evidence for separate mechanisms. Cognitive Neuropsychology 17, 1, 35--55.Google ScholarCross Ref
- S. Boehm and W. Sommer. 2005. Neural correlates of intentional and incidental recognition of famous faces. Cognitive Brain Research 23, 2--3, 153--163.Google ScholarCross Ref
- H. Bojinov, D. Sanchez, P. Reber, D. Boneh, and P. Lincoln. 2012. Neuroscience meets cryptography: Designing crypto primitives secure against rubber hose attacks. In 21st USENIX Security Symposium. Bellevue, WA, 129--141. Google ScholarDigital Library
- T. Bonaci, J. Herron, C. Matlack, and H. J. Chizeck. 2014. Securing the exocortex: A twenty-first century cybernetics challenge. In IEEE Conference on Norbert Wiener in the 21st Century (21CW’14). Boston, MA, 1--8.Google Scholar
- L. Bougrain, C. Saavedra, and R. Ranta. 2012. Finally, what is the best filter for P300 detection? In TOBI Workshop lll- Tools for Brain-Computer Interaction. Würzburg, Germany.Google Scholar
- C. Brunner, N. Birbaumer, B. Blankertz, C. Guger, A. Kübler, D. Mattia, J. Millán, F. Miralles, A. Nijholt, E. Opisso, N. Ramsey, P. Salomon, and G. R. Müller-Putz. 2015. BNCI horizon 2020: Towards a roadmap for the BCI community. Brain-Computer Interfaces 1--10. http://dx.doi.org/10.1080/2326263X.2015.1008956Google Scholar
- S. Caharel, S. Poiroux, C. Bernard, F. Thibaut, R. Lalonde, and M. Rebai. 2002. ERPs associated with familiarity and degree of familiarity during face recognition. International Journal of Neuroscience 112, 12, 1499--1512.Google ScholarCross Ref
- J. Chuang, H. Nguyen, C. Wang, and B. Johnson. 2013. I think, therefore I am: Usability and security of authentication using brainwaves. In Workshop on Usable Security (USEC’13).Google Scholar
- J. Clausen. 2014. Ethical implications of brain-computer interfacing. In Handbook of Neuroethics, J. Clausen and N. Levy (Eds.). Springer, The Netherlands, 699--704.Google Scholar
- A. M. Cleary. 2002. Recognition with and without identification: Dissociative effects of meaningful encoding. Memory & Cognition 30, 5 (2002), 758--767.Google ScholarCross Ref
- A. M. Cleary and R. L. Greene. 2000. Recognition without identification. Experimental Psychology: Learning, Memory & Cognition 26, 1063--1069.Google ScholarCross Ref
- A. M. Cleary, K. E. Konkel, J. S. Nomi, and D. P. McCabe. 2010. Odor recognition without identification. Memory & Cognition 38, 4, 452--460.Google ScholarCross Ref
- T. Currana and A. M. Cleary. 2003. Using ERPs to dissociate recollection from familiarity in picture recognition. Cognitive Brain Research 15, 191--205.Google ScholarCross Ref
- J. Debruille, F. Guillem, and B. Renault. 1995. ERPs and chronometry of face recognition: Following-up Seeck et al. and George et al. Neuroreport 9, 15, 3349--3353.Google ScholarCross Ref
- R. Dhamija and A. Perrig. 2000. Déjà Vu: A user study using images for authentication. In 9th USENIX Security Symposium. Google ScholarDigital Library
- M. Eimer. 2000. Event-related brain potentials distinguish processing stages involved in face perception and recognition. Clinical Neurophysiology 111, 4, 694--705.Google ScholarCross Ref
- Emotiv. 2016. EPOC. Retrieved August 2, 2016 from https://emotiv.com/epoc.php.Google Scholar
- T. Fawcett. 2006. An introduction to ROC analysis. Pattern Recognition Letters 27, 8, 861--874. Google ScholarDigital Library
- R. A. Fisher. 1936. The use of multiple measurements in taxonomic problems. Annals of Eugenics 7, 2, 179--188.Google ScholarCross Ref
- M. Frank, T. Hwu, S. Jain, R. Knight, I. Martinovic, P. Mittal, D. Perito, and D. Song. 2013. Subliminal probing for private information via EEG-based BCI devices. CoRR abs/1312.6052 (2013).Google Scholar
- N. George, B. Jemel, N. Fiori, and B. Renault. 1997. Face and shape repetition effects in humans: A spatio-temporal ERP study. Neuroreport 8, 6, 1417--1423.Google ScholarCross Ref
- A. G. Greenwald, T. A. Poehlman, E. L. Uhlmann, and M. R. Banaji. 2009. Understanding and using the implicit association test: III. Meta-analysis of predictive validity. Personality and Social Psychology 97, 1, 17--41.Google ScholarCross Ref
- InteraXon. 2016. Muse: The Brain Sensing Headband. Retrieved August 2, 2016 from http://www.choosemuse.com/.Google Scholar
- H. H. Jasper. 1958. The ten twenty electrode system of the international federation. Electroencephalography and Clinical Neurophysiology 10, 371--375.Google Scholar
- B. Johnson, T. Maillart, and J. Chuang. 2014. My thoughts are not your thoughts. In 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication. ACM, Seattle, WA, 1329--1338. Google ScholarDigital Library
- M. Kaper, P. Meinicke, U. Grossekathoefer, T. Lingner, and H. Ritter. 2004. BCI competition 2003-data set IIb: Support vector machines for the P300 speller paradigm. IEEE Transactions on Biomedical Engineering 51, 1073--1076.Google ScholarCross Ref
- B. Kostic and A. M. Cleary. 2009. Song recognition without identification: When people cannot “name that tune” but can recognize it as familiar. Experimental Psychology: General 138, 1, 146--159.Google ScholarCross Ref
- D. J. Krusienski, E. W. Sellers, F. Cabestaing, S. Bayoudh, and D. J. Mcfarland. 2006. A comparison of classification techniques for the P300 speller. Journal of Neural Engineering 3, 4, 299--305.Google ScholarCross Ref
- J. C. Lee and D. S. Tan. 2006. Using a low-cost electroencephalograph for task classification in HCI research. In Proceedings of the 19th Annual ACM Symposium on User Interface Software and Technology (UIST’06). 81--90. Google ScholarDigital Library
- S. Marcel and J. Millán. 2007. Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 4, 743--752. Google ScholarDigital Library
- I. Martinovic, D. Davies, M. Frank, D. Perito, T. Ros, and D. Song. 2012. On the feasibility of side-channel attacks with brain-computer interfaces. In 21st USENIX Security Symposium. Bellevue, WA, 143--158. Google ScholarDigital Library
- E. Mnatsakanian and I. Tarkka. 2003. Matching of familiar faces and abstract patterns: Behavioral and high-resolution ERP study. International Journal of Psychophysiology 47, 3, 217--227.Google ScholarCross Ref
- M. Mustafa, L. Lindemann, and M. Magnor. 2012. EEG analysis of implicit human visual perception. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’12). 513--516. Google ScholarDigital Library
- A. Neupane, N. Saxena, K. Kuruvilla, M. Georgescu, and R. Kana. 2014. Neural signatures of user-centered security: An fMRI study of phishing, and malware warnings. In Network and Distributed Systems Security Symposium (NDSS’14).Google Scholar
- NeuroSky. 2016a. EEG Hardware Platforms. Retrieved August 2, 2016 from http://neurosky.com/biosensors/eeg-sensor/biosensors/.Google Scholar
- NeuroSky. 2016b. MindFlex Duel. Retrieved August 2, 2016 from http://store.neurosky.com/products/mindflex-duel.Google Scholar
- M. E. J. Newman. 2005. Power laws, Pareto distributions and Zipf’s law. Contemporary Physics 46, 323--351.Google ScholarCross Ref
- Passfaces Co. 2016. Passfaces: Two Factor Authentication for the Enterprise.. Retrieved August 2, 2016 from http://www.passfaces.com/index.htm.Google Scholar
- E. M. M. Peck, B. F. Yuksel, A. Ottley, R. J. K. Jacob, and R. Chang. 2013. Using fNIRS brain sensing to evaluate information visualization interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’13). 473--482. Google ScholarDigital Library
- E.-M. Pfütze, W. Sommer, and S. R. Schweinberger. 2002. Age-related slowing in face and name recognition: Evidence from event-related brain potentials. Psychology and Aging 17, 140--160.Google ScholarCross Ref
- T. Rosburg, P. Trautner, T. Dietl, T. Kral, C. E. Elger, and M. Kurthen. 2005. The influence of repetition and famousness on the intracranially recorded temporobasal N200. Behavioural Neuroscience 119, 4, 876--883.Google ScholarCross Ref
- J. L. Sanguinetti, John J. B. Allen, and M. A. Peterson. 2014. The ground side of an object: Perceived as shapeless yet processed for semantics. Psychological Science 25, 1, 256--264.Google ScholarCross Ref
- Schalk Lab. 2016. BCI2000 Wiki.. Retrieved August 2, 2016 from http://www.bci2000.org/wiki/index.php.Google Scholar
- M. Seeck, C. M. Michel, N. Mainwaring, R. Cosgrove, H. Blume, J. Ives, T. Landis, and D. L. Schomer. 1997. Evidence for rapid face recognition from human scalp and intracranial electrodes. Cognitive Neuroscience and Neuropsychology 8, 12, 2749--2754.Google Scholar
- S. Shalgi and L. Y. Deouell. 2012. Is any awareness necessary for an Ne? Frontiers in Human Neuroscience 6, 124.Google ScholarCross Ref
- H. P. D. Silva, S. Fairclough, A. Holzinger, R. Jacob, and D. Tan. 2015. Introduction to the special issue on physiological computing for human-computer interaction. ACM Transactions on Computer-Human Interaction 21, 6, 29:1--29:4. Google ScholarDigital Library
- E. T. Solovey, F. Lalooses, K. Chauncey, D. Weaver, M. Parasi, M. Schuetz, A. Sassaroli, S. Fantini, P. Schermerhorn, A. Girouard, and R. J. K. Jacob. 2011. Sensing cognitive multitasking for a brain-based adaptive user interface. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’11). 383--392. Google ScholarDigital Library
- L. Standing. 1973. Learning 10000 pictures. Quarterly Journal of Experimental Psychology 25, 2, 207--222.Google ScholarCross Ref
- S. Sternberg. 1966. High-speed scanning in human memory. Science 153, 3736, 652--654.Google Scholar
- J. R. Stroop. 1935. Studies of interference in serial verbal reactions. Journal of Experimental Psychology 18, 6, 643--662.Google ScholarCross Ref
- S. Sutton, M. Braren, J. Zubin, and E. R. John. 1965. Evoked-potential correlates of stimulus uncertainty. Science 150, 1187--1188.Google ScholarCross Ref
- D. Szafir and B. Mutlu. 2012. Pay attention! Designing adaptive agents that monitor and improve user engagement. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’12). 11--20. Google ScholarDigital Library
- D. J. Watts. 1999. Small Worlds: The Dynamics of Networks Between Order and Randomness. Princeton University Press, Princeton, NJ. Google ScholarDigital Library
- B. F. Yuksel, M. Donnerer, J. Tompkin, and A. Steed. 2010. A novel brain-computer interface using a multi-touch surface. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’10). 855--858. Google ScholarDigital Library
Index Terms
- Detection of Subconscious Face Recognition Using Consumer-Grade Brain-Computer Interfaces
Recommendations
Have We Met Before? Using Consumer-Grade Brain-Computer Interfaces to Detect Unaware Facial Recognition
Special Issue: Deep Learning, Ubiquitous and Toy ComputingMuch research has been done on the brain’s reaction to seeing faces, but while much of the work has investigated the brain’s conscious reaction to faces, far less work has been done exploring the brain’s unaware reactions using consumer-grade devices. ...
Face recognition under varying illumination using gradientfaces
In this correspondence, we propose a novel method to extract illumination insensitive features for face recognition under varying lighting called the Gradientfaces. Theoretical analysis shows Gradientfaces is an illumination insensitive measure, and ...
Age-Invariant Face Recognition
One of the challenges in automatic face recognition is to achieve temporal invariance. In other words, the goal is to come up with a representation and matching scheme that is robust to changes due to facial aging. Facial aging is a complex process that ...
Comments