Abstract
Most of the biometric techniques are conventional biometrics employing some neural network architecture consisting of suitable number of hidden layers in their training process. The recognition accuracy has no longer become a measure to ensure the method of recognition is robust. Robustness has also been attempted by using hybrid methods for training such as neuro-fuzzy method and applying some optimization technique. The traditional use of the learning concept has almost been saturated in the field of computer vision and face recognition. An emerging concept of learning method that is being researched internationally in several cognitive, computer vision and data classification tasks is deep learning, which is subfield of machine learning inspired by structure and function of artificial neural network. This paper suggests using deep learning in assessment of cognitive ability of human brain. We plan to train thousands of facial images into our image database. Deep learning was compared to shallow learning in face recognition task done for real time applications; cognitive ability assessment and inference for different age group and gender; study of reaction time, etc. A sample size of 380 persons was tested in real time deep learning based face recognition. Response time and correct identification were recorded that shows potent research scope of deep learning in assessment of cognitive ability of human brain at large scale. The cognitive ability of women was found more than that of women.
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Sinha, G.R. Study of assessment of cognitive ability of human brain using deep learning. Int. j. inf. tecnol. 9, 321–326 (2017). https://doi.org/10.1007/s41870-017-0025-8
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DOI: https://doi.org/10.1007/s41870-017-0025-8