Disclosed is a system and method for obtaining optical properties of skin on a human face through face video analysis. Video of the face is captured, landmarks on the face and tracked, regions-of-interest are defined and tracked using the landmarks, some measurements/optical properties are obtained, the time-based video is transformed into an angular domain, and additional measurements/optical properties are obtained. Such optical properties can be measured using video in real-time or video that has been pre-recorded.

Methods, systems, an apparatus, including computer programs encoded on a storage device, for training an image classifier. A method includes receiving an image that includes a depiction of an object; generating a set of poorly localized bounding boxes; and generating a set of accurately localized bounding boxes. The method includes training, at a first learning rate and using the poorly localized bounding boxes, an object classifier to classify the object; and training, at a second learning rate that is lower than the first learning rate, and using the accurately localized bounding boxes, the object classifier to classify the object. The method includes receiving a second image that includes a depiction of an object; and providing, to the trained object classifier, the second image. The method includes receiving an indication that the object classifier classified the object in the second image; and performing one or more actions.

A method for detecting at least one biometric trait visible in an input image, by means of a convolutional neural network, the method wherein it comprises the implementation, by data processing means of a client, of steps of: (a) Generating, by means of a feature extraction block of said CNN, a plurality of representation vectors each defining a candidate region of interest of said input image potentially containing a biometric trait, the representation vector of a candidate region of interest comprising at least one position value of the candidate region of interest, at least one size value of the candidate region of interest, an orientation value of the candidate region of interest, and an objectivity score of the candidate region of interest; (b) Selecting, by means of a filtering block of said CNN, at least one region of interest from said candidate regions based on the representation vectors thereof.

A method for detecting at least one biometric trait visible in an input image, by means of a convolutional neural network, the method wherein it comprises the implementation, by data processing means of a client, of steps of: (a) Generating, by means of a feature extraction block of said CNN, a plurality of representation vectors each defining a candidate region of interest of said input image potentially containing a biometric trait, the representation vector of a candidate region of interest comprising at least one position value of the candidate region of interest, at least one size value of the candidate region of interest, an orientation value of the candidate region of interest, and an objectivity score of the candidate region of interest; (b) Selecting, by means of a filtering block of said CNN, at least one region of interest from said candidate regions based on the representation vectors thereof.

Provided is a method for generating web codes for a user interface (UI) based on a generative adversarial network (GAN) and a convolutional neural network (CNN). The method includes steps described below. A mapping relationship between display effects of a HyperText Markup Language (HTML) element and source codes of the HTML element is constructed. A location of an HTML element in an image I is recognized. Complete HTML codes of the image I are generated. The similarity between manually-written HTML codes and the generated complete HTML codes and the similarity between the image I and an image I.sub.1 generated by the generated complete HTML codes are obtained. After training, an image-to-HTML-code generation model M is obtained. A to-be-processed UI image is input into the model M so as to obtain corresponding HTML codes. According to the method of the present disclosure, an image-to-HTML-code generation model M can be obtained.

Provided is a method for generating web codes for a user interface (UI) based on a generative adversarial network (GAN) and a convolutional neural network (CNN). The method includes steps described below. A mapping relationship between display effects of a HyperText Markup Language (HTML) element and source codes of the HTML element is constructed. A location of an HTML element in an image I is recognized. Complete HTML codes of the image I are generated. The similarity between manually-written HTML codes and the generated complete HTML codes and the similarity between the image I and an image I.sub.1 generated by the generated complete HTML codes are obtained. After training, an image-to-HTML-code generation model M is obtained. A to-be-processed UI image is input into the model M so as to obtain corresponding HTML codes. According to the method of the present disclosure, an image-to-HTML-code generation model M can be obtained.

The information processing apparatus (2000) includes a feature point detection unit (2020), a determination unit (2040), an extraction unit (2060), and a comparison unit (2080). A feature point detection unit (2020) detects a plurality of feature points from the query image. The determination unit (2040) determines, for each feature point, one or more object images estimated to include the feature point. The extraction unit (2060) extracts an object region estimated to include the object in the query image in association with the object image of the object estimated to be included in the object region, on the basis of the result of the determination. The comparison unit (2080) cross-checks the object region with the object image associated with the object region and determines an object included in the object region.

The information processing apparatus (2000) includes a feature point detection unit (2020), a determination unit (2040), an extraction unit (2060), and a comparison unit (2080). A feature point detection unit (2020) detects a plurality of feature points from the query image. The determination unit (2040) determines, for each feature point, one or more object images estimated to include the feature point. The extraction unit (2060) extracts an object region estimated to include the object in the query image in association with the object image of the object estimated to be included in the object region, on the basis of the result of the determination. The comparison unit (2080) cross-checks the object region with the object image associated with the object region and determines an object included in the object region.

A method and processor system are provided which analyze a depth map, which may be obtained from a range sensor capturing depth information of a scene, to identify where an object is located in the scene. Accordingly, a region of interest may be identified in the scene which includes the object, and image data may be selectively obtained of the region of interest, rather than of the entire scene containing the object. This image data may be acquired by an image sensor configured for capturing visible light information of the scene. By only selectively obtaining the image data within the region of interest, rather than all of the image data, improvements may be realized in the computational complexity of a possible further processing of the image data, the storage of the image data and/or the transmission of the image data.

A method and processor system are provided which analyze a depth map, which may be obtained from a range sensor capturing depth information of a scene, to identify where an object is located in the scene. Accordingly, a region of interest may be identified in the scene which includes the object, and image data may be selectively obtained of the region of interest, rather than of the entire scene containing the object. This image data may be acquired by an image sensor configured for capturing visible light information of the scene. By only selectively obtaining the image data within the region of interest, rather than all of the image data, improvements may be realized in the computational complexity of a possible further processing of the image data, the storage of the image data and/or the transmission of the image data.