Abstract: An autofocus method includes acquiring multiple images each having a camera lens focused at a different focus distance. A sharpest image is determined among the multiple images. Horizontal, vertical and/or diagonal integral projection (IP) vectors are computed for each of the multiple images. One or more IP vectors of the sharpest image is/are convoluted with multiple filters of different lengths to generate one or more filtered IP vectors for the sharpest image. Differences are computed between the one or more filtered IP vectors of the sharpest image and one or more IP vectors of at least one of the other images of the multiple images. At least one blur width is estimated between the sharpest image and the at least one of the other images of the multiple images as a minimum value among the computed differences over a selected range. The steps are repeated one or more times to obtain a sequence of estimated blur width values. A focus position is adjusted based on the sequence of estimated blur width values.

Abstract: A technique for determining a characteristic of a face or certain other object within a scene captured in a digital image including acquiring an image and applying a linear texture model that is constructed based on a training data set and that includes a class of objects including a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components which are independent of directional lighting variations. A fit of the model to the face or certain other object is obtained including adjusting one or more individual values of one or more of the model components of the linear texture model. Based on the obtained fit of the model to the face or certain other object in the scene, a characteristic of the face or certain other object is determined.

Abstract: A method and device is provided for adjusting the white balance of a digital image by adjusting the values assigned to the red, green, and blue subpixels of a pixel in the image. The adjustment to the subpixels is determined by identifying pixels in the image that have an RGB product greater than a threshold value, wherein the threshold value is based at least in part on an average of the RGB products of each pixel in the image and a variance between the RGB products of the pixels and the average of the RGB products.

Abstract: A method and apparatus for providing image processing. For one embodiment of the invention, a digital image is acquired. One or more relatively large candidate red eye defect regions are detected in at least a portion of the image. Face detection is applied to at least a portion of the image to eliminate non-face regions and one or more relatively small candidate red eye defect regions are identified in at least a portion of the image not including the eliminated non-face regions.

Abstract: A face illumination normalization method includes acquiring a digital image including a face that appears to be illuminated unevenly. One or more uneven illumination classifier programs are applied to the face data to determine the presence of the face within the digital image and/or the uneven illumination condition of the face. The uneven illumination condition may be corrected to thereby generate a corrected face image appearing to have more uniform illumination, for example, to enhance face recognition.

Abstract: A method and device for detecting a potential defect in an image comprises acquiring a digital image at a time; storing image acquisition data, wherein the image acquisition data includes at least one of a position of a source of light relative to a lens, a distance from the source of light to the lens, a focal length of the lens, a distance from a point on a digital image acquisition device to a subject, an amount of ambient light, or flash intensity; determining dynamic anthropometric data, wherein the dynamic anthropometric data includes one or more dynamically changing human body measurements, of one or more humans represented in the image, captured at the time; and determining a course of corrective action based, at least in part, on the image acquisition data and the dynamic anthropometric data.

Abstract: An image processing apparatus for tracking faces in an image stream iteratively receives an acquired image from the image stream including one or more face regions. The acquired image is sub-sampled at a specified resolution to provide a sub-sampled image. An integral image is then calculated for a least a portion of the sub-sampled image. Fixed size face detection is applied to at least a portion of the integral image to provide a set of candidate face regions. Responsive to the set of candidate face regions produced and any previously detected candidate face regions, the resolution is adjusted for sub-sampling a subsequent acquired image.

Abstract: An image processing apparatus for tracking faces in an image stream iteratively receives an acquired image from the image stream including one or more face regions. The acquired image is sub-sampled at a specified resolution to provide a sub-sampled image. An integral image is then calculated for a least a portion of the sub-sampled image. Fixed size face detection is applied to at least a portion of the integral image to provide a set of candidate face regions. Responsive to the set of candidate face regions produced and any previously detected candidate face regions, the resolution is adjusted for sub-sampling a subsequent acquired image.

Abstract: An image processing apparatus for tracking faces in an image stream iteratively receives an acquired image from the image stream including one or more face regions. The acquired image is sub-sampled at a specified resolution to provide a sub-sampled image. An integral image is then calculated for a least a portion of the sub-sampled image. Fixed size face detection is applied to at least a portion of the integral image to provide a set of candidate face regions. Responsive to the set of candidate face regions produced and any previously detected candidate face regions, the resolution is adjusted for sub-sampling a subsequent acquired image.

Abstract: A method and device for detecting a potential defect in an image comprises acquiring a digital image at a time; storing image acquisition data, wherein the image acquisition data includes at least one of a position of a source of light relative to a lens, a distance from the source of light to the lens, a focal length of the lens, a distance from a point on a digital image acquisition device to a subject, an amount of ambient light, or flash intensity; determining dynamic anthropometric data, wherein the dynamic anthropometric data includes one or more dynamically changing human body measurements, of one or more humans represented in the image, captured at the time; and determining a course of corrective action based, at least in part, on the image acquisition data and the dynamic anthropometric data.

Abstract: Different distances of two or more objects in a scene being captured in a video conference are determined by determining a sharpest of two or more color channels and calculating distances based on the determining of the sharpest of the two or more color channels. At least one of the objects is identified as a foreground object or a background object, or one or more of each, based on the determining of the different distances. The technique involves blurring or otherwise rendering unclear at least one background object or one or more portions of the scene other than the at least one foreground object, or combinations thereof, also based on the determining of distances.

Abstract: A device is trained for face recognition. A first acquired digital image of a scene includes a face. Face image data is extracted and stored in a face image library along with an unique identifier. A second acquired digital image may or may not include the face of the same person as the face in the first acquired digital image. Face recognition is applied to extracted face data of the second digital image, and the face of the first digital image is displayed as a match to the face of the second digital image when the first and second images are determined to match or the second digital image or a face therein is identified with biometric data stored along with the first digital image when the faces in the first and second images are determined to match.

Abstract: A method of processing a digital image using face detection within the image achieves one or more desired image processing parameters. A group of pixels is identified that correspond to an image of a face within the digital image. Default values are determined of one or more parameters of at least some portion of the digital image. Values are adjusted of the one or more parameters within the digitally-detected image based upon an analysis of the digital image including the image of the face and the default values.

Abstract: A method of generating one or more new spatial and chromatic variation digital images uses an original digitally-acquired image which including a face or portions of a face. A group of pixels that correspond to a face within the original digitally-acquired image is identified. A portion of the original image is selected to include the group of pixels. Values of pixels of one or more new images based on the selected portion are automatically generated, or an option to generate them is provided, in a manner which always includes the face within the one or more new images. Such method may be implemented to automatically establish the correct orientation and color balance of an image. 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 filtering a red-eye phenomenon from an acquired digital image including a multiplicity of pixels indicative of color, the pixels forming various shapes of the image, includes analyzing meta-data information, determining one or more regions within the digital image suspected as including red eye artifact, and determining, based at least in part on the meta-data analysis, whether the regions are actual red eye artifact. The meta-data information may include information describing conditions under which the image was acquired, captured and/or digitized, acquisition device-specific information, and/film information.

Abstract: Sub-regions within a face image are identified to be enhanced by applying a localized smoothing kernel to luminance data corresponding to the sub-regions of the face image. An enhanced face image is generated including an enhanced version of the face that includes certain original pixels in combination with pixels corresponding to the one or more enhanced sub-regions of the face.

Abstract: A method for detecting a face in a mid-shot digital image of a person comprises capturing first and second mid-shot digital images of nominally the same scene using different capture settings such that the foreground is differently differentiated from the background in each image, and comparing the first and second images to determine the foreground region of the images. A portion of the foreground region likely to correspond to a face is estimated based upon the geometry of the foreground region.

Abstract: An image registration method involves computing horizontal and vertical integral projection vectors for first and second distorted or partially distorted images or distortion-corrected images, or both. The images are registered by applying a translation, rotation and/or scale factor estimation between the first and second images on the horizontal and vertical integral projection vectors.

Abstract: A digital image acquisition system includes a portable apparatus for capturing digital images and a digital processing component for detecting, analyzing and informing the photographer regarding motion blur, and for reducing camera motion blur in an image captured by the apparatus. The digital processing component operates by comparing the image with at least one other image, for example a preview image, of nominally the same scene taken outside the exposure period of the main image. In one embodiment the digital processing component determines the degree of artifacts and whether to inform the user that the image is blurred by identifying at least one feature in a single preview image which is relatively less blurred than the corresponding feature in the main image.

Abstract: A method of filtering a red-eye phenomenon from an acquired digital image including a multiplicity of pixels indicative of color, the pixels forming various shapes of the image, includes analyzing meta-data information, determining one or more regions within the digital image suspected as including red eye artifact, and determining, based at least in part on the meta-data analysis, whether the regions are actual red eye artifact. The meta-data information may include information describing conditions under which the image was acquired, captured and/or digitized, acquisition device-specific information, and/film information.