ISPUB.COM The Internet Journal of Radiology Volume 20 Number 1 DOI: 10.5580/IJRA.49126 1 of 7 Face Recognition, Reversible Correlation Between fMRI and Biometrics Data D H Marks, A Yildiz, S Vural, S Levy Citation D H Marks, A Yildiz, S Vural, S Levy. Face Recognition, Reversible Correlation Between fMRI and Biometrics Data. The Internet Journal of Radiology. 2017 Volume 20 Number 1. DOI: 10.5580/IJRA.49126 Abstract Specific individual face recognition in the brain has been demonstrated with analysis of three dimensional neural activation patterns – cognitive engrams – revealed by functional magnetic resonance imaging (fMRI). Individual faces can also be differentiated by biometric pattern recognition from camera images using biometric analysis. A correlation between face recognition data obtained from these two methods is now documented. A two way correspondence between face data obtained by these and other means exists, which should facilitate face recognition, the utility of interrogation, and further the understanding of cognition. INTRODUCTION Since 2006, it has been known that widely arranged brain cortical response patterns elicited by individual face images with high-resolution functional magnetic resonance imaging (fMRI) can be used to discriminate between unique faces (1). This work has been independently validated by other research laboratories (2,3). Face activation patterns obtained by fMRI are known to be related to and vary with the structure of the face (4) and these variations are consistent across individuals. Cognitive engrams refer to multi-dimensional representations of brain activation in response to specific stimuli (1). Cognitive engrams can be arranged into a [Rosetta] database which relates the Cognitive Engrams and other associated data to specific mental concepts, i.e., a visual representation of actual memory patterns. Faces can also be analyzed and correlated with their physical features (5,6,7). Relative sizes and distances for facial landmarks such as the eyes, nose, ears, chin, and skin texture, among others can be measured. Face data can be extracted from camera images, or from video streams. Principle methods for biometric face analysis are geometric, which is feature based, and photometric, which is view based. Many different algorithms for face analysis have been developed, including principal component analysis PCA, linear discriminant analysis LDA, elastic Bunch graph matching EBGM, and more recently deep-learning (DL) based non-linear feature extraction methods. Face structure, as are all of our physical characteristics, are coded within DNA. Claes et al. (8) used extensive modeling methods to determine the relationships between facial variation and the effects of sex, genomic ancestry, and a subset of craniofacial candidate genes. Their modeling could lead to approximating the appearance of a face from genetic markers alone. Knowing that face recognition by fMRI and by biometrics both depend on the physical differences between individual faces, a correlation of face recognition from these two different data sources was studied and reported herein. METHODS Overall, the steps used in this study are: Test subjects view pictures of face, object, or concept, or has other visual or auditory stimulation, while undergoing functional neuroimaging, Functional neuroimaging data is collected, 3-D Activation map is constructed, which constitutes the specific Cognitive Engram for the face / object imaged, Collection of activation maps is added to a (Rosetta) Database of activation maps The same faces used for generating fMRI activation maps are examined by (video) camera, and a biometric analysis is generated, One-to-one correspondence is made between the fMRI activation map (the facial cognitive engram)