Abstract
Object recognition has always been one of the key areas of research in modern times, especially in healthcare and engineering industry, because of the wide range of applications it has. There have been various methods to recognize and classify objects like shape matching, color matching, sliding window approach, etc. but a common problem the computational models face is the appropriate representation of 3D objects, image variation with angle variation, illumination effects, and the high computational costs of these models to maintain output accuracy. In this paper, we propose a novel method to detect and analyze the process of object recognition from magnetoencephalogram (MEG) signals of human brain. This could be made possible by classifying the object as face/scrambled face using machine learning through support vector machine (SVM). We train our SVM model using the recordings in DecMeg Human Brain dataset obtained from Kaggle. In addition, by calculating the accuracy of individual sensors for the duration, we are able to identify the cluster of sensors responsible for visual recognition and the dynamic interaction among sensors with the passage of time using neural coordinates of the magnetometer sensors with an accuracy of 74.85%. We found that the sensors in the occipitotemporal and occipitoparietal lobes are most actively involved in visual classification. The proposed approach has been able to reduce the previous effective time stamp of 100–360 ms to 124–240 ms. This reduces the computational cost of the model while establishing the essential relationships between MEG signals and facial detection.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Burton, A.M., Wilson, S., Cowan, M., Bruce, V.: Face recognition in poor-quality video: evidence from security surveillance. Psychol. Sci. 10, 243–248 (1999)
Wu, C.-C., Zeng, Y.-C., Shih, M.-J.: Enhancing retailer marketing with an facial recognition integrated recommender system. IEEE (2015)
Stone, Z., Zickler, T., Darrell, T.: Autotagging facebook: social network context improves photo annotation. In: Workshop on Internet Vision (2008)
Boucenna, S., Gaussier, P., Andry, P., Hafemeister, L.: A robot learns the facial expressions recognition and face/non-face discrimination through an imitation game. Int. J. Soc. Robot. (2014)
Lei, Z., Wang, C., Wang, Q., Huang, Y.: Real-time face detection and recognition for video surveillance applications. In: 2009 World Congress on Computer Science and Information Engineering, pp. 168–172. (2009)
Shergill, G.H., Sarrafzadeh, H., Diegel, O., Shekar, A.: Computerized sales assistants: the application of computer technology to measure consumer interest;a conceptual framework. J. Electron. Commer. Res. 9(2), 176191 (2008)
Klin, A., Sparrow, S.S. De Bildt, A., Cicchetti, D.V., Cohen, D.J., Volkmar, F.R.: A normed study of face recognition in autism and related disorders. J. Autism Dev. Disord. 499–508 (1999)
Grter, T., Grter, M., Carbon, C.C.: Neural and genetic foundations of face recognition and prosopagnosia. J. Neuropsychol. 79–97 (2008)
Pierce, K., Mller, R.-A., Ambrose, J., Allen, G., Courchesne, E.: Face processing occurs outside the fusiform ‘face area’ in autism: evidence from functional MRI. Brain 124(10), 2059–2073 (2001)
Bruce, V., Young, A.: Understanding face recognition. Br. J. Psychol. 77(3), 305–327 (1986)
Baron, R.J.: Mechanisms of human facial recognition, Int . J. Man-Mach. Stud. 15, 137–178 (1981)
Le, T.H.: Applying artificial neural networks for face recognition. In: Advances in Artificial Neural Systems (2011)
Turk, M., Pentland, A.: Face recognition using eigenfaces. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)
Yang, G., Huang, T.S.: Human face detection in a complex background. Pattern Recognit. 27, 53–63 (1994)
Khurana, P., Sharma, A., Singh, SN., Singh, P.K.: A survey on object recognition and segmentation techniques. In: 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 3822–3826 (2016)
CS229: Machine Learning, retrieved from chapter notes on Support Vector Machines. http://cs229.stanford.edu/syllabus.html
Manning, C., Raghavan, P., Schtze, H.: Introduction to Information Retrieval. Cambridge University Press, Chapter 15 (2008)
DecMeg2014: Decoding the Humain Brain. https://www.kaggle.com/c/decoding-the-human-brain/data
Streit, M., Dammers, J., Simsek-Kraues, S., Brinkmeyer, J., Wolwer, W., Ioannides, A.: Time course of regional brain activations during facial emotion recognition in humans. Neurosci. Lett. 342(12), 101–104 (2003)
Acknowledgements
We would like to acknowledge Kaggle for providing us with human brain signal dataset for facilitation of the research proposed in this paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gupta, S., Gandhi, T. (2020). Identification of Neural Correlates of Face Recognition Using Machine Learning Approach. In: Gupta, M., Konar, D., Bhattacharyya, S., Biswas, S. (eds) Computer Vision and Machine Intelligence in Medical Image Analysis. Advances in Intelligent Systems and Computing, vol 992. Springer, Singapore. https://doi.org/10.1007/978-981-13-8798-2_2
Download citation
DOI: https://doi.org/10.1007/978-981-13-8798-2_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8797-5
Online ISBN: 978-981-13-8798-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)