Abstract: A method of iris recognition comprises detecting a body region larger than and comprising at least one iris in an image and performing a first eye modelling on the detected body region. If successful, the result of first iris segmentation based on the first eye model is chosen. Otherwise, a first iris identification is performed on the detected body region. If successful, the result of second iris segmentation based on a second eye modelling is chosen. Otherwise, second iris identification is performed on the image, third eye modelling is performed on the result of the second iris identification, and third iris segmentation is performed on the result of the third eye modelling. If successful, the result of third iris segmentation based on a third eye modelling is chosen. An iris code is extracted from any selected iris segment of the image.

Abstract: A method of iris recognition comprises detecting a body region larger than and comprising at least one iris in an image and performing a first eye modelling on the detected body region. If successful, the result of first iris segmentation based on the first eye model is chosen. Otherwise, a first iris identification is performed on the detected body region. If successful, the result of second iris segmentation based on a second eye modelling is chosen. Otherwise, second iris identification is performed on the image, third eye modelling is performed on the result of the second iris identification, and third iris segmentation is performed on the result of the third eye modelling. If successful, the result of third iris segmentation based on a third eye modelling is chosen. An iris code is extracted from any selected iris segment of the image.

Abstract: A method of iris recognition comprises detecting a body region larger than and comprising at least one iris in an image and performing a first eye modelling on the detected body region. If successful, the result of first iris segmentation based on the first eye model is chosen. Otherwise, a first iris identification is performed on the detected body region. If successful, the result of second iris segmentation based on a second eye modelling is chosen. Otherwise, second iris identification is performed on the image, third eye modelling is performed on the result of the second iris identification, and third iris segmentation is performed on the result of the third eye modelling. If successful, the result of third iris segmentation based on a third eye modelling is chosen. An iris code is extracted from any selected iris segment of the image.

Abstract: A method of iris recognition comprises detecting a body region larger than and comprising at least one iris in an image and performing a first eye modelling on the detected body region. If successful, the result of first iris segmentation based on the first eye model is chosen. Otherwise, a first iris identification is performed on the detected body region. If successful, the result of second iris segmentation based on a second eye modelling is chosen. Otherwise, second iris identification is performed on the image, third eye modelling is performed on the result of the second iris identification, and third iris segmentation is performed on the result of the third eye modelling. If successful, the result of third iris segmentation based on a third eye modelling is chosen. An iris code is extracted from any selected iris segment of the image.

Abstract: A method of making an image in a digital camera comprises capturing a digital image of a scene into which the camera user is to be inserted, and superimposing a symbol (subject locator) onto the scene image representing at least a part of a human subject. The subject locator is scaled to a desired size and moved to a desired position relative to the scene image. Next a digital image of the user is captured, and at least the part of the user image represented by the subject locator is extracted. The part of the user image represented by the subject locator is scaled (before or after extraction) to substantially the same size as the subject locator and inserted into the first image at the position of the subject locator. In a second embodiment the method comprises displaying a preview image of a scene into which the camera user is to be inserted, and superimposing the subject locator on the preview image.

Abstract: A technique is provided for recognizing faces in an image stream using a digital image acquisition device. A first acquired image is received from an image stream. A first face region is detected within the first acquired image having a given size and a respective location within the first acquired image. First faceprint data uniquely identifying the first face region are extracted along with first peripheral region data around the first face region. The first faceprint and peripheral region data are stored, and the first peripheral region data are associated with the first face region. The first face region is tracked until a face lock is lost. A second face region is detected within a second acquired image from the image stream. Second peripheral region data around the second face region are extracted. The second face region is identified upon matching the first and second peripheral region data.

Abstract: A method of detecting a face in an image includes performing face detection within a first window of the image at a first location. A confidence level is obtained from the face detection indicating a probability of the image including a face at or in the vicinity of the first location. Face detection is then performed within a second window at a second location, wherein the second location is determined based on the confidence level.

Abstract: A method of detecting a face in an image includes performing face detection within a first window of the image at a first location. A confidence level is obtained from the face detection indicating a probability of the image including a face at or in the vicinity of the first location. Face detection is then performed within a second window at a second location, wherein the second location is determined based on the confidence level.

Abstract: A method of detecting a face in an image includes performing face detection within a first window of the image at a first location. A confidence level is obtained from the face detection indicating a probability of the image including a face at or in the vicinity of the first location. Face detection is then performed within a second window at a second location, wherein the second location is determined based on the confidence level.

Abstract: A technique is provided for recognizing faces in an image stream using a digital image acquisition device. A first acquired image is received from an image stream. A first face region is detected within the first acquired image having a given size and a respective location within the first acquired image. First faceprint data uniquely identifying the first face region are extracted along with first peripheral region data around the first face region. The first faceprint and peripheral region data are stored, and the first peripheral region data are associated with the first face region. The first face region is tracked until a face lock is lost. A second face region is detected within a second acquired image from the image stream. Second peripheral region data around the second face region are extracted. The second face region is identified upon matching the first and second peripheral region data.

Abstract: A method of detecting a face in an image includes performing face detection within a first window of the image at a first location. A confidence level is obtained from the face detection indicating a probability of the image including a face at or in the vicinity of the first location. Face detection is then performed within a second window at a second location, wherein the second location is determined based on the confidence level.

Abstract: A method of detecting a face in an image includes performing face detection within a first window of the image at a first location. A confidence level is obtained from the face detection indicating a probability of the image including a face at or in the vicinity of the first location. Face detection is then performed within a second window at a second location, wherein the second location is determined based on the confidence level.

Abstract: A method of detecting a face in an image includes performing face detection within a first window of the image at a first location. A confidence level is obtained from the face detection indicating a probability of the image including a face at or in the vicinity of the first location. Face detection is then performed within a second window at a second location, wherein the second location is determined based on the confidence level.

Abstract: A technique is provided for recognizing faces in an image stream using a digital image acquisition device. A first acquired image is received from an image stream. A first face region is detected within the first acquired image having a given size and a respective location within the first acquired image. First faceprint data uniquely identifying the first face region are extracted along with first peripheral region data around the first face region. The first faceprint and peripheral region data are stored, and the first peripheral region data are associated with the first face region. The first face region is tracked until a face lock is lost. A second face region is detected within a second acquired image from the image stream. Second peripheral region data around the second face region are extracted. The second face region is identified upon matching the first and second peripheral region data.

Abstract: A method and system for image recognition in a collection of digital images includes training image classifiers and retrieving a sub-set of images from the collection. For each image in the collection, any regions within the image that correspond to a face are identified. For each face region and any associated peripheral region, feature vectors are determined for each of the image classifiers. The feature vectors are stored in association with data relating to the associated face region. At least one reference region including a face to be recognized is/are selected from an image. At least one classifier on which said retrieval is to be based is/are selected from the image classifiers. A respective feature vector for each selected classifier is determined for the reference region. The sub-set of images is retrieved from within the image collection in accordance with the distance between the feature vectors determined for the reference region and the feature vectors for face regions of the image collection.

Abstract: A technique is provided for recognizing faces in an image stream using a digital image acquisition device. A first acquired image is received from an image stream. A first face region is detected within the first acquired image having a given size and a respective location within the first acquired image. First faceprint data uniquely identifying the first face region are extracted along with first peripheral region data around the first face region. The first faceprint and peripheral region data are stored, and the first peripheral region data are associated with the first face region. The first face region is tracked until a face lock is lost. A second face region is detected within a second acquired image from the image stream. Second peripheral region data around the second face region are extracted. The second face region is identified upon matching the first and second peripheral region data.

Abstract: A method of detecting a face in an image includes performing face detection within a first window of the image at a first location. A confidence level is obtained from the face detection indicating a probability of the image including a face at or in the vicinity of the first location. Face detection is then performed within a second window at a second location, wherein the second location is determined based on the confidence level.

Abstract: A method and system for image recognition in a collection of digital images includes training image classifiers and retrieving a sub-set of images from the collection. For each image in the collection, any regions within the image that correspond to a face are identified. For each face region and any associated peripheral region, feature vectors are determined for each of the image classifiers. The feature vectors are stored in association with data relating to the associated face region. At least one reference region including a face to be recognized is/are selected from an image. At least one classifier on which said retrieval is to be based is/are selected from the image classifiers. A respective feature vector for each selected classifier is determined for the reference region. The sub-set of images is retrieved from within the image collection in accordance with the distance between the feature vectors determined for the reference region and the feature vectors for face regions of the image collection.

Abstract: A method and system for image recognition in a collection of digital images includes training image classifiers and retrieving a sub-set of images from the collection. For each image in the collection, any regions within the image that correspond to a face are identified. For each face region and any associated peripheral region, feature vectors are determined for each of the image classifiers. The feature vectors are stored in association with data relating to the associated face region. At least one reference region including a face to be recognized is/are selected from an image. At least one classifier on which said retrieval is to be based is/are selected from the image classifiers. A respective feature vector for each selected classifier is determined for the reference region. The sub-set of images is retrieved from within the image collection in accordance with the distance between the feature vectors determined for the reference region and the feature vectors for face regions of the image collection.

Abstract: A method and system for image recognition in a collection of digital images includes training image classifiers and retrieving a sub-set of images from the collection. For each image in the collection, any regions within the image that correspond to a face are identified. For each face region and any associated peripheral region, feature vectors are determined for each of the image classifiers. The feature vectors are stored in association with data relating to the associated face region. At least one reference region including a face to be recognized is/are selected from an image. At least one classifier on which said retrieval is to be based is/are selected from the image classifiers. A respective feature vector for each selected classifier is determined for the reference region. The sub-set of images is retrieved from within the image collection in accordance with the distance between the feature vectors determined for the reference region and the feature vectors for face regions of the image collection.