Provided is a method for recognizing face by using a multiple patch combination based on a deep neural network, and improving fault tolerance and property robust to vibration in an extreme situation. More specifically, the present invention relates to a method for recognizing a face using multiple patch combination based on a deep neural network, which comprises: a step (a) of allowing a face recognizing device to input a face image to a feature extracting network when the face image having a first size is obtained, wherein the feature extracting network is learned to extract at least one feature by using a learning face image having a second size and the second size is smaller than the first size, and allowing the feature extracting network to apply at least one convolutional operation to the face image having the first size so as to generate a feature map, and to apply a sliding pooling operation to the feature map to generate a plurality of features; and a step (b) of allowing the face recognizing device to input the features to a neural aggregation network, and allowing the neural aggregation network to output at least one optimal feature for face recognition by aggregating the features.
deep neural network multiple patch combination METHOD FOR RECOGNIZING FACE USING MULTIPLE PATCH COMBINATION BASED ON DEEP NEURAL NETWORK WITH FAULT TOLERANCE AND FLUCTUATION ROBUSTNESS IN EXTREME SITUATION