Abstract: Techniques for identifying a person in a target image are described. According to one of the techniques, identifying a person in a target image involves displaying, within a graphical user interface, an image that depicts one or more faces. One or more faces are automatically detected within the image. A user provides input that selects a face of the one or more faces to be a currently-selected face. A set of images are selected from a collection of images, where the set of images includes images that closely match the currently-selected face. Concurrently with display of the currently-selected face, each image in the set of images is displayed. Within the graphical user interface, a control is provided. The control enables a user to select a target image from the set of images. In response to detecting that the user has selected a target image using the control, the currently-selected face is associated with a person to which the target image corresponds.

Abstract: Techniques for identifying a person in a target image are described. According to one of the techniques, identifying a person in a target image involves displaying, within a graphical user interface, an image that depicts one or more faces. One or more faces are automatically detected within the image. A user provides input that selects a face of the one or more faces to be a currently-selected face. A set of images are selected from a collection of images, where the set of images includes images that closely match the currently-selected face. Concurrently with display of the currently-selected face, each image in the set of images is displayed. Within the graphical user interface, a control is provided. The control enables a user to select a target image from the set of images. In response to detecting that the user has selected a target image using the control, the currently-selected face is associated with a person to which the target image corresponds.

Abstract: A processor-based system operating according to digitally-embedded programming instructions performs a method including identifying a group of pixels corresponding to a face region within digital image data acquired by an image acquisition device. A set of face analysis parameter values is extracted from said face region, including a faceprint associated with the face region. First and second reference faceprints are determined for a person using reference images captured respectively in predetermined face-portrait conditions and using ambient conditions. The faceprints are analyzed to determine a baseline faceprint and a range of variability from the baseline associated with the person. Results of the analyzing are stored and used in subsequent recognition of the person in a subsequent image acquired under ambient conditions.

Abstract: A digital image processing technique is for detecting and correcting visual imperfections using a reference image. A main image and one or more reference images having a temporal and/or spatial overlap and/or proximity with the original image are captured. Device information, image data and/or meta data are analyzed of the one or more reference images relating to a defect in the main image. The device corrects the defect based on the information, image data and/or meta-data to create an enhanced version of the main image.

Abstract: A processor-based system operating according to digitally-embedded programming instructions performs a method including identifying a group of pixels corresponding to a face region within digital image data acquired by an image acquisition device. A set of face analysis parameter values is extracted from said face region, including a faceprint associated with the face region. First and second reference faceprints are determined for a person using reference images captured respectively in predetermined face-portrait conditions and using ambient conditions. The faceprints are analyzed to determine a baseline faceprint and a range of variability from the baseline associated with the person. Results of the analyzing are stored and used in subsequent recognition of the person in a subsequent image acquired under ambient conditions.

Abstract: A method of automatically establishing the correct orientation of an image using facial information. This method is based on the exploitation of the inherent property of image recognition algorithms in general and face detection in particular, where the recognition is based on criteria that is highly orientation sensitive. By applying a detection algorithm to images in various orientations, or alternatively by rotating the classifiers, and comparing the number of successful faces that are detected in each orientation, one may conclude as to the most likely correct orientation. Such method can be implemented as an automated method or a semi automatic method to guide users in viewing, capturing or printing of images.

Abstract: A processor-based system operating according to digitally-embedded programming instructions performs a method including identifying a group of pixels corresponding to a face region within digital image data acquired by an image acquisition device. A set of face analysis parameter values is extracted from said face region, including a faceprint associated with the face region. First and second reference faceprints are determined for a person using reference images captured respectively in predetermined face-portrait conditions and using ambient conditions. The faceprints are analyzed to determine a baseline faceprint and a range of variability from the baseline associated with the person. Results of the analyzing are stored and used in subsequent recognition of the person in a subsequent image acquired under ambient conditions.

Abstract: A method of automatically establishing the correct orientation of an image using facial information. This method is based on the exploitation of the inherent property of image recognition algorithms in general and face detection in particular, where the recognition is based on criteria that is highly orientation sensitive. By applying a detection algorithm to images in various orientations, or alternatively by rotating the classifiers, and comparing the number of successful faces that are detected in each orientation, one may conclude as to the most likely correct orientation. Such method can be implemented as an automated method or a semi automatic method to guide users in viewing, capturing or printing of images.

Abstract: A technique for detecting large and small non-red eye flash defects in an image is disclosed. The method comprises selecting pixels of the image which have a luminance above a threshold value and labeling neighboring selected pixels as luminous regions. A number of geometrical filters are applied to the luminous regions to remove false candidate luminous regions.

Abstract: A method of automatically establishing the correct orientation of an image using facial information. This method is based on the exploitation of the inherent property of image recognition algorithms in general and face detection in particular, where the recognition is based on criteria that is highly orientation sensitive. By applying a detection algorithm to images in various orientations, or alternatively by rotating the classifiers, and comparing the number of successful faces that are detected in each orientation, one may conclude as to the most likely correct orientation. Such method can be implemented as an automated method or a semi automatic method to guide users in viewing, capturing or printing of images.

Abstract: Flash image orb artifacts arising from specular reflections from airborne particles are corrected. A specific location is detected within a digital image of a flash image airborne particle artifact (orb). A defined curved plane shape is electronically identified within the image. Luminances are analyzed of pixels within the identified shape to assess whether the shape in fact corresponds to an orb. The digital image is corrected by removing the orb. One or more pixel values are adjusted inside the orb, and one or more edge pixel values of the orb are also adjusted.

Abstract: A method of automatically establishing the correct orientation of an image using facial information. This method is based on the exploitation of the inherent property of image recognition algorithms in general and face detection in particular, where the recognition is based on criteria that is highly orientation sensitive. By applying a detection algorithm to images in various orientations, or alternatively by rotating the classifiers, and comparing the number of successful faces that are detected in each orientation, one may conclude as to the most likely correct orientation. Such method can be implemented as an automated method or a semi automatic method to guide users in viewing, capturing or printing of images.

Abstract: A method of automatically establishing the correct orientation of an image using facial information. This method is based on the exploitation of the inherent property of image recognition algorithms in general and face detection in particular, where the recognition is based on criteria that is highly orientation sensitive. By applying a detection algorithm to images in various orientations, or alternatively by rotating the classifiers, and comparing the number of successful faces that are detected in each orientation, one may conclude as to the most likely correct orientation. Such method can be implemented as an automated method or a semi automatic method to guide users in viewing, capturing or printing of images.

Abstract: A method of automatically establishing the correct orientation of an image using facial information. This method is based on the exploitation of the inherent property of image recognition algorithms in general and face detection in particular, where the recognition is based on criteria that is highly orientation sensitive. By applying a detection algorithm to images in various orientations, or alternatively by rotating the classifiers, and comparing the number of successful faces that are detected in each orientation, one may conclude as to the most likely correct orientation. Such method can be implemented as an automated method or a semi automatic method to guide users in viewing, capturing or printing of images.

Abstract: A method of automatically establishing the correct orientation of an image using facial information. This method is based on the exploitation of the inherent property of image recognition algorithms in general and face detection in particular, where the recognition is based on criteria that is highly orientation sensitive. By applying a detection algorithm to images in various orientations, or alternatively by rotating the classifiers, and comparing the number of successful faces that are detected in each orientation, one may conclude as to the most likely correct orientation. Such method can be implemented as an automated method or a semi automatic method to guide users in viewing, capturing or printing of images.

Abstract: Flash image orb artifacts arising from specular reflections from airborne particles are corrected. An image of a scene using flash illumination and a reference image of substantially the same scene are acquired. The reference and flash images are subtracted to generate a difference image. Edge and interior region features of an orb artifact within the difference image are determined. The edge feature is matched with the interior region, and the orb artifact is corrected from the flash image based on the matching.

Abstract: A digital image processing technique is for detecting and correcting visual imperfections using a reference image. A main image and one or more reference images having a temporal and/or spatial overlap and/or proximity with the original image are captured. Device information, image data and/or meta data are analyzed of the one or more reference images relating to a defect in the main image. The device corrects the defect based on the information, image data and/or meta-data to create an enhanced version of the main image.

Abstract: Flash image orb artifacts arising from specular reflections from airborne particles are corrected. A specific location is detected within a digital image of a flash image airborne particle artifact (orb). A defined curved plane shape is electronically identified within the image. Luminances are analyzed of pixels within the identified shape to assess whether the shape in fact corresponds to an orb. The digital image is corrected by removing the orb. One or more pixel values are adjusted inside the orb, and one or more edge pixel values of the orb are also adjusted.

Abstract: Flash image orb artifacts arising from specular reflections from airborne particles are corrected. An image of a scene using flash illumination and a reference image of substantially the same scene are acquired. The reference and flash images are subtracted to generate a difference image. Edge and interior region features of an orb artifact within the difference image are determined. The edge feature is matched with the interior region, and the orb artifact is corrected from the flash image based on the matching.

Abstract: A technique for detecting large and small non-red eye flash defects in an image is disclosed. The method comprises selecting pixels of the image which have a luminance above a threshold value and labeling neighboring selected pixels as luminous regions. A number of geometrical filters are applied to the luminous regions to remove false candidate luminous regions.