Face recognition and image search system using sparse feature vectors, compact binary vectors, and sub-linear search

A system, for searching a database of gallery binary vectors representing gallery images of faces, the system comprising one or more servers for enrollment and searching and memory, the memory storing instructions that, when executed by the one or more servers, cause the one or more servers to:

• receive an input image of a face;
• process the received input image of the face, comprising:
• processing through a plurality of layers of a deep neural network (DNN) to generate a k-dimensional feature vector;
• and
• binarizing the feature vector to generate a k-dimensional binary vector by converting respective values in the feature vector to corresponding values of 0 or 1 in the binary vector, wherein the conversion is based on whether respective values in the feature vector satisfy predefined criteria;
• divide the binary vector into a plurality of sub-strings;
• create a plurality of hash tables based on gallery sub-strings of gallery binary vectors;
• and
• compare one or more of the sub-strings of the binary vector to the one or more of the hash tables to identify a subset of the gallery binary vectors that are closest matches to the binary vector.

A non-transitory computer-readable storage medium storing instructions that, when executed by one or more servers for enrollment and searching, cause the one or more servers to:

• receive an input image of a face;
• process the received input image of the face, comprising:
• processing through a plurality of layers of a deep neural network (DNN) to generate a k-dimensional feature vector;
• and
• binarizing the feature vector to generate a k-dimensional binary vector by converting respective values in the feature vector to corresponding values of 0 or 1 in the binary vector, wherein the conversion is based on whether respective values in the feature vector satisfy predefined criteria;
• divide the binary vector into a plurality of sub-strings;
• create a plurality of hash tables based on gallery sub-strings of gallery binary vectors;
• and
• compare one or more of the sub-strings of the binary vector to the one or more of the hash tables to identify a subset of the gallery binary vectors that are closest matches to the binary vector.