Abstract: Devices and methods for evaluating at least one photograph during a photography session are described. In some instances, a camera may be used to capture images during a photography session and a computing device may be used to identify a portrait order specification for the photography session including a set of required photographs, each having associated required criteria. The computing device may evaluate the images and determine whether the images can be associated with any of the required photographs based on features included in the images being associated with the required criteria of the required photographs. The images associated with required photographs may be displayed, and a determination of whether the images qualify as a required photograph may be made using a graphical user interface, or may be made automatically.

Abstract: A method of identifying a background type in a photograph includes extracting a background image from a photograph, feeding the background image into a first convolution neural network to obtain a first decision, extracting color features in the background image, transforming the color features into a two-dimensional color feature matrix, feeding the two-dimensional color feature matrix into a second convolution neural network to obtain a second decision by the one or more computer processors, extracting texture features in the background image, transforming the texture features into a two-dimensional texture feature matrix image by the one or more computer processors, feeding the two-dimensional texture feature matrix into a third convolution neural network to obtain a third decision, computing a hybrid decision based on the first decision, the second decision, and the third decision, and identifying a background type in the background image based on the hybrid decision.

Abstract: A computer implemented method for recognizing facial expressions by applying feature learning and feature engineering to face images. The method includes conducting feature learning on a face image comprising feeding the face image into a first convolution neural network to obtain a first decision, conducting feature engineering on a face image, comprising the steps of automatically detecting facial landmarks in the face image, transforming the facial features into a two-dimensional matrix, and feeding the two-dimensional matrix into a second convolution neural network to obtain a second decision, computing a hybrid decision based on the first decision and the second decision, and recognizing a facial expression in the face image in accordance to the hybrid decision.

Abstract: A computer implemented method for recognizing facial expressions by applying feature learning and feature engineering to face images. The method includes conducting feature learning on a face image comprising feeding the face image into a first convolution neural network to obtain a first decision, conducting feature engineering on a face image, comprising the steps of automatically detecting facial landmarks in the face image, transforming the facial features into a two-dimensional matrix, and feeding the two-dimensional matrix into a second convolution neural network to obtain a second decision, computing a hybrid decision based on the first decision and the second decision, and recognizing a facial expression in the face image in accordance to the hybrid decision.

Abstract: A computer implemented method for recognizing facial expressions by applying feature learning and feature engineering to face images. The method includes conducting feature learning on a face image comprising feeding the face image into a first convolution neural network to obtain a first decision, conducting feature engineering on a face image, comprising the steps of automatically detecting facial landmarks in the face image, transforming the facial features into a two-dimensional matrix, and feeding the two-dimensional matrix into a second convolution neural network to obtain a second decision, computing a hybrid decision based on the first decision and the second decision, and recognizing a facial expression in the face image in accordance to the hybrid decision.

Abstract: A method of identifying a background type in a photograph includes extracting a background image from a photograph, feeding the background image into a first convolution neural network to obtain a first decision, extracting color features in the background image, transforming the color features into a two-dimensional color feature matrix, feeding the two-dimensional color feature matrix into a second convolution neural network to obtain a second decision by the one or more computer processors, extracting texture features in the background image, transforming the texture features into a two-dimensional texture feature matrix image by the one or more computer processors, feeding the two-dimensional texture feature matrix into a third convolution neural network to obtain a third decision, computing a hybrid decision based on the first decision, the second decision, and the third decision, and identifying a background type in the background image based on the hybrid decision.

Abstract: Devices and methods for conducting a remote photography session are described. In some instances, a computing device at a photography station receives messages from a photography station controller which is remote from the photography station. In some examples, the computing device receives one or more messages instructing the computing device to capture an image from an image capture device. The computing device can also receive one or more messages instructing the computing device to adjust the image capture device. Additionally, the messages may instruct the computing device to present instructions to a subject of the photography session. These instructions can prompt the subject to make adjustments to meet a criteria for a photograph in the photography session.

Abstract: A method of identifying a background type in a photograph includes extracting a background image from a photograph, feeding the background image into a first convolution neural network to obtain a first decision, extracting color features in the background image, transforming the color features into a two-dimensional color feature matrix, feeding the two-dimensional color feature matrix into a second convolution neural network to obtain a second decision by the one or more computer processors, extracting texture features in the background image, transforming the texture features into a two-dimensional texture feature matrix image by the one or more computer processors, feeding the two-dimensional texture feature matrix into a third convolution neural network to obtain a third decision, computing a hybrid decision based on the first decision, the second decision, and the third decision, and identifying a background type in the background image based on the hybrid decision.

Abstract: A computer implemented method for gender classification by applying feature learning and feature engineering to face images. The method includes conducting feature learning on a face image comprising feeding the face image into a first convolution neural network to obtain a first decision, conducting feature engineering on a face image, comprising the steps of automatically detecting facial landmarks in the face image, transforming the facial features into a two-dimensional matrix, and feeding the two-dimensional matrix into a second convolution neural network to obtain a second decision, computing a hybrid decision based on the first decision and the second decision, and classifying gender of the face image in accordance with the hybrid decision.

Abstract: A method of photographing a subject includes storing a library of photographic scene designs in a computer memory, training a photographic scene detection model by a computer processing device using machine learning from sample portrait images comprising known photographic scenes defined in the library of photographic scene designs, capturing a production portrait photograph, using a digital camera, of a subject in a photographic scene that is defined by a photographic scene design in the library of photographic scene designs, automatically detecting the photographic scene in the production portrait photograph using the photographic scene detection model operating on one or more computer processors, and processing the production portrait photograph by an image processing system to personalize the photographic scene detected in the production portrait photograph.

Abstract: Devices and methods for evaluating at least one photograph during a photography session are described. In some instances, a camera may be used to capture images during a photography session and a computing device may be used to identify a portrait order specification for the photography session including a set of required photographs, each having associated required criteria. The computing device may evaluate the images and determine whether the images can be associated with any of the required photographs based on features included in the images being associated with the required criteria of the required photographs. The images associated with required photographs may be displayed, and a determination of whether the images qualify as a required photograph may be made using a graphical user interface, or may be made automatically.

Abstract: Devices and methods for evaluating at least one photograph during a photography session are described. In some instances, a camera may be used to capture images during a photography session and a computing device may be used to identify a portrait order specification for the photography session including a set of required photographs, each having associated required criteria. The computing device may evaluate the images and determine whether the images can be associated with any of the required photographs based on features included in the images being associated with the required criteria of the required photographs. The images associated with required photographs may be displayed, and a determination of whether the images qualify as a required photograph may be made using a graphical user interface, or may be made automatically.

Abstract: Devices and methods for evaluating at least one photograph during a photography session are described. In some instances, a camera may be used to capture images during a photography session and a computing device may be used to identify a portrait order specification for the photography session including a set of required photographs, each having associated required criteria. The computing device may evaluate the images and determine whether the images can be associated with any of the required photographs based on features included in the images being associated with the required criteria of the required photographs. The images associated with required photographs may be displayed, and a determination of whether the images qualify as a required photograph may be made using a graphical user interface, or may be made automatically.

Abstract: A computer implemented method for recognizing facial expressions by applying feature learning and feature engineering to face images. The method includes conducting feature learning on a face image comprising feeding the face image into a first convolution neural network to obtain a first decision, conducting feature engineering on a face image, comprising the steps of automatically detecting facial landmarks in the face image, transforming the facial features into a two-dimensional matrix, and feeding the two-dimensional matrix into a second convolution neural network to obtain a second decision, computing a hybrid decision based on the first decision and the second decision, and recognizing a facial expression in the face image in accordance to the hybrid decision.

Abstract: A method of identifying a background type in a photograph includes extracting a background image from a photograph, feeding the background image into a first convolution neural network to obtain a first decision, extracting color features in the background image, transforming the color features into a two-dimensional color feature matrix, feeding the two-dimensional color feature matrix into a second convolution neural network to obtain a second decision by the one or more computer processors, extracting texture features in the background image, transforming the texture features into a two-dimensional texture feature matrix image by the one or more computer processors, feeding the two-dimensional texture feature matrix into a third convolution neural network to obtain a third decision, computing a hybrid decision based on the first decision, the second decision, and the third decision, and identifying a background type in the background image based on the hybrid decision.

Abstract: A computer implemented method for recognizing facial expressions by applying feature learning and feature engineering to face images. The method includes conducting feature learning on a face image comprising feeding the face image into a first convolution neural network to obtain a first decision, conducting feature engineering on a face image, comprising the steps of automatically detecting facial landmarks in the face image, transforming the facial features into a two-dimensional matrix, and feeding the two-dimensional matrix into a second convolution neural network to obtain a second decision, computing a hybrid decision based on the first decision and the second decision, and recognizing a facial expression in the face image in accordance to the hybrid decision.

Abstract: A computer implemented method for gender classification by applying feature learning and feature engineering to face images. The method includes conducting feature learning on a face image comprising feeding the face image into a first convolution neural network to obtain a first decision, conducting feature engineering on a face image, comprising the steps of automatically detecting facial landmarks in the face image, transforming the facial features into a two-dimensional matrix, and feeding the two-dimensional matrix into a second convolution neural network to obtain a second decision, computing a hybrid decision based on the first decision and the second decision, and classifying gender of the face image in accordance with the hybrid decision.

Abstract: A method of photographing a subject includes storing a library of photographic scene designs in a computer memory, training a photographic scene detection model by a computer processing device using machine learning from sample portrait images comprising known photographic scenes defined in the library of photographic scene designs, capturing a production portrait photograph, using a digital camera, of a subject in a photographic scene that is defined by a photographic scene design in the library of photographic scene designs, automatically detecting the photographic scene in the production portrait photograph using the photographic scene detection model operating on one or more computer processors, and processing the production portrait photograph by an image processing system to personalize the photographic scene detected in the production portrait photograph.