Pose-invariant face recognition system and process

A face recognition system for identifying a person depicted in an input image, comprising:

• a general purpose computing device;
• and
• a computer program comprising program modules executable by the computing device, wherein the computing device is directed by the program modules of the computer program to,
• capture model images, each of which depicts at least one person of known identity,
• locate and extract regions within the model images, each of which depicts the face of a known person that it is desired to identify in the input image,
• determine a face pose for each of the face regions extracted from the model images, wherein the face pose refers to pitch, roll and yaw angles that define the position of a person's head,
• categorize each face region by assigning each to one of a set of pose ranges into which its associated face pose falls,
• train a neural network ensemble to identify a person and their face pose from a region that depicts the face of a person which has been extracted from said input image, wherein the network ensemble comprises, a first stage having a plurality of classifiers each of which has input and output units and is dedicated to a particular pose range and outputs a measure of the similarity indicative of the similarity between said input image region and each of said model image regions associated with the particular pose range of the classifier, and a fusing neural network as its second stage which combines the outputs of the classifiers to generate an output indicative of the person associated with the characterized input image region and the face pose of that person;
• and
• employ the network ensemble to identify the person associated with the characterized input image region and their face pose.

A computer-readable memory for use in identifying a person depicted in an input image, comprising:

• a computer-readable storage medium;
• and
• a computer program comprising program modules stored in the storage medium, wherein the storage medium is so configured by the computer program that it causes a computer to,
• input model images, each of which depicts at least one person of known identity,
• locate and extract regions within the model images, each of which depicts the face of a known person that it is desired to identify in the input image,
• determine a face pose for each of the face regions extracted from the model images, wherein the face pose refers to pitch, roll and yaw angles that define the position of a person's head,
• categorize each face region by assigning each to one of a set of pose ranges into which its associated face pose falls,
• train a neural network ensemble to identify a person and their face pose from a region depicting the face of a person which has been extracted from said input image, wherein the network ensemble comprises, a first stage having a plurality of classifiers each of which has input and output units and is dedicated to a particular pose range and outputs a measure of the similarity indicative of the similarity between said input image region and each of said model image regions associated with the particular pose range of the classifier, and a fusing neural network as its second stage which combines the outputs of the classifiers to generate an output indicative of the person associated with the characterized input image region and the face pose of that person;
• and
• employ the network ensemble to identify the person associated with the characterized input image region and their face pose.

A face recognition neural network ensemble for identifying a person depicted in an input image and a face pose range among a set of pose ranges into which the face of each identified person falls, wherein a face pose range refers to ranges of pitch, roll and yaw angles that define the position of a person's head, said ensemble comprising:

• a plurality of face recognition neural networks each of which has input and output units and each of which is dedicated to a particular pose range;
• and
• a fusing neural network whose inputs are in communication with the output units of said face recognition neural networks and which has at least enough output units to allow a different output to represent each person it is desired to identify at each of the pose ranges;
• and wherein
• image feature characterizations derived from the face of a person it is desired to identify as depicted in the input image are respectively input into separate ones of the input units of the face recognition neural networks causing a single one of the output units of the fusing neural network to become active, thereby indicating the identity of the person whose face was depicted in the input image as well as the pose range associated with the pose of the depicted face.

A face recognition network ensemble for identifying a person depicted in an input image and a face pose range among a set of pose ranges into which the face of each identified person falls, wherein a face pose range refers to ranges of pitch, roll and yaw angles that define the position of a person's head, said ensemble comprising:

• a plurality of classifiers each of which has input and output units and each of which is dedicated to a particular pose range;
• and
• a fusing neural network whose inputs are in communication with the output units of said classifiers and which has at least enough output units to allow a different output to represent each person it is desired to identify at each of the pose ranges;
• and wherein
• image feature characterizations derived from the face of a person it is desired to identify as depicted in the input image are respectively input into separate ones of the input units of the face recognition classifiers causing a single one of the output units of the fusing neural network to become active, thereby indicating the identity of the person whose face was depicted in the input image as well as the pose range associated with the pose of the depicted face.