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In the booming era of face recognition, various challenges are arising owe to the changes in the characteristics of biometric identifications. In order to handle the uncontrolled situations caused by the variations of light, there are many algorithms are in practice. To capture the image with light intensity and also to detect the travelling path of light ray, plenoptic camera is used. The proposed work uses a framework of double-deep spatio-angular learning to recognize light filed based images that are the sample of spatial and angular information with the sequence of two deep networks. This proposed framework added the inputs from VGG-Face descriptions and computed the VGG-19 based convolutional neural network. The 2D Sub Aperture(SA) images, that are derived from the images captured from plenoptic camera are used to extract the VGG-Face spatial descriptions in various angles. Then Long-Short Term Memory(LSTM) is used to analyze the VGG-face spatial description sequence.The images are then compared with the data base and the result obtained result is efficient and accurate when compared to existing system.