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Twin identification over viewpoint change: A deep convolutional neural network surpasses humans
These findings also contribute to our understanding of DCNN performance for discriminating high-resemblance faces, demonstrate that the DCNN performs at a level at or above humans, and suggest a degree of parity between the features used by humans and the DCNN.
The Influence of the Other-Race Effect on Susceptibility to Face Morphing Attacks
Similar to humans, the DCNN performed more accurately for original face images than for morphed image pairs.
Single Unit Status in Deep Convolutional Neural Network Codes for Face Identification: Sparseness Redefined
Therefore, distributed and sparse codes co-exist in the network units to represent different face attributes.
Deep Convolutional Neural Networks in the Face of Caricature: Identity and Image Revealed
Deep convolutional neural networks (DCNNs) also create generalizable face representations, but with cascades of simulated neurons.
Deep Convolutional Neural Network Features and the Original Image
The results show that the DCNN features contain surprisingly accurate information about the yaw and pitch of a face, and about whether the face came from a still image or a video frame.
