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Parameterisation of a stochastic model for human face ...
DOI: 10.1109/ACV.1994.341300 Corpus ID: 2153469. Parameterisation of a stochastic model for human face identification @article{Samaria1994ParameterisationOA, title={Parameterisation of a stochastic model for human face identification}, author={Ferdinand Samaria and Andy Harter}, journal={Proceedings of 1994 IEEE Workshop on Applications of Computer Vision}, year={1994}, pages={138-142} }...
Dynamic face recognition: From human to machine vision ...
Much is known about the neural systems that subserve face recognition in adult humans and primates. Face-selective neurons have been found in the inferior temporal areas (TEa and TEm), the superior temporal sensory area, the amygdala, the ventral striatum (which receives input from the amygdala) and the inferior convexity .Using functional magnetic resonance imaging (fMRI), an area in the ......
Human Recognition Using Face in Computed Tomography | DeepAI
Human Recognition Using Face in Computed Tomography. ... we focus on the face area in CT, which occupies the majority of CT images, especially in head and neck CT. ... Finally, we leverage transfer learning to adapt a pretrained face recognition model from face depth images in the RGB-D domain to the computed face depth images from CTs....
Wavelet based Artificial Light Receptor – A feature ...
A feature vector of size 40 is formed for face images of each person and recognition accuracy is computed using k-NN classifier. Key Words : Face recognition, Image analysis, Wavelet feature extraction, Pattern recognition, k-NN Classifier. INTRODUCTION Face recognition is a task that humans perform routinely and effortlessly in daily life....
 Expression-invariant Facial Identification
Facial recognition has received substantial attention in the ... tackle the ‘one sample problem’ and the expression-invariant ... for converting a smiling face to a neutral face by using a triangular mesh model (CANDIDE) for generic human face and Active Appearance Models (AAMs) to find the optimal ......
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