Abstract: A line-of-sight detection apparatus includes a detection unit configured to detect a face from image data, a first extraction unit configured to extract a feature amount corresponding to a direction of the face from the image data, a calculation unit configured to calculate a line-of-sight reliability of each of a right eye and a left eye based on the face, a selection unit configured to select an eye according to the line-of-sight reliability, a second extraction unit configured to extract a feature amount of an eye region of the selected eye from the image data, and an estimation unit configured to estimate a line of sight of the face based on the feature amount corresponding to the face direction and the feature amount of the eye region.

Abstract: An image processing method is provided for an image processing apparatus which executes processing by allocating a plurality of weak discriminators to form a tree structure having branches corresponding to types of objects so as to detect objects included in image data. Each weak discriminator calculates a feature amount to be used in a calculation of an evaluation value of the image data, and discriminates whether or not the object is included in the image data by using the evaluation value. The weak discriminator allocated to a branch point in the tree structure further selects a branch destination using at least some of the feature amounts calculated by weak discriminators included in each branch destination.

Abstract: An image processing apparatus stores model information representing a subject model belonging to a specific category, detects the subject from an input image by referring to the model information, determines a region for which an image correction is to be performed within a region occupied by the detected subject in the input image, stores, for a local region of the image, a plurality of correction data sets representing correspondence between a feature vector representing a feature before correction and a feature vector representing a feature after correction, selects at least one of the correction data sets to be used to correct a local region included in the region determined to undergo the image correction, and corrects the region determined to undergo the image correction using the selected correction data sets.

Abstract: An object identification apparatus includes an image data input unit configured to input captured image data including an object, an object identification data generation unit configured to generate data for identifying the object by extracting a feature vector from a partial area of the input image data to convert the feature vector according to the partial area, an object dictionary data storage unit configured to store object dictionary data generated from previously recorded image data, and an object identification unit configured to identify a class to which the object belongs, which is included in the image data input by the image data input unit, based on the data for identifying the object and the object dictionary data.

Abstract: An information processing apparatus comprises: a creation unit adapted to create a query string, which is composed of a plurality of queries; a reading unit adapted to read, from the query string, a query corresponding to a hierarchy of a current node in accordance with the hierarchy; a generation unit adapted to generate a plurality of nodes having a tree structure by dividing a learning pattern set in the current node in accordance with the read query corresponding to the hierarchy; and a modifying unit adapted to modify the tree structure by putting nodes in each hierarchy together into a common node with respect to nodes up to a hierarchy corresponding to a length of the query string among the nodes having the tree structure, which have been generated by the generation unit.

Abstract: The present invention refers to an information processing apparatus comprising: an obtaining unit adapted to obtain an image of an object; a face region detection unit adapted to detect a face region of the object from the image; an eye region detection unit adapted to detect an eye region of the object; a generation unit adapted to generate a high-resolution image and low-resolution image of the face region detected by the face region detection means; a first extraction unit adapted to extract a first feature amount indicating a direction of a face existing in the face region from the low-resolution image; a second extraction unit adapted to extract a second feature amount indicating a direction of an eye existing in the eye region from the high-resolution image; and an estimation unit adapted to estimate a gaze direction of the object from the first feature amount and the second feature amount.

Abstract: An apparatus includes a capturing unit that captures a target object and generates a range image representing distance information, a general estimation unit that analyzes the range image and estimates a general position and orientation of the target object, and a determination unit that determines a priority order of a plurality of identification units, based on the estimated general position and orientation. In addition, a setting unit sets a search window indicating a range for detailed estimation of position and orientation of the range image based on a preregistered general size of the target object, a calculation unit calculates a difference between a first distance value of a first pixel in the range image and a second distance value of a second pixel which is next to the first pixel in the range image, and an updating unit updates the search window based on the calculated difference.

Abstract: An information processing apparatus that discriminates the orientation of a target includes a calculation unit that calculates a distribution of a difference in feature amount between a plurality of learning patterns each showing the orientation of a target, a determination unit that determines, using a probability distribution obtained from the distribution of differences calculated by the calculation unit, a pixel that is in an input pattern and is to be referred to in order to discriminate the orientation of a target in the input pattern, and a discrimination unit that performs discrimination for obtaining the orientation of the target in the input pattern by comparing a feature amount of the pixel determined by the determination unit and a threshold set in advance.

Abstract: An object identification apparatus, which identifies a class of a plurality of registered classes to which an object included in an input image belongs, registers a plurality of registered images used in identification of an input image, and holds designation data required to designate partial areas used in identification. The apparatus extracts feature amounts of the partial areas designated by the designation data from the input image and each of the plurality of registered images, and identifies a class to which the input image belongs based on the extracted feature amounts. The apparatus determines based on the plurality of registered images whether or not is required to update processing contents of an identification unit. When it is determined that the processing contents are required to be updated, the apparatus updates the designation data based on registered images.

Abstract: A pattern recognition method, applicable to input information including a plurality of regions, includes obtaining a certainty at which each region of the input information includes a pattern, selecting one or more regions having a relatively high-level certainty among the plurality of regions, and performing pattern detection processing on the selected region or regions.

Abstract: A feature point positioning apparatus positions a plurality of feature points for a predetermined pattern in image data. The apparatus selectively executes first candidate decision processing which decides position candidates of the feature points and second candidate decision processing which decides position candidates of the feature points at a higher processing speed than the first candidate decision processing in accordance with an operation mode, and executes, when the operation mode is a high-speed mode in which an operation is executed at a higher speed than a normal mode, the second candidate decision processing for more feature points than in the normal mode; and corrects the position candidates of the plurality of feature points obtained by the first candidate decision processing and the second candidate decision processing based on a layout relationship among the plurality of feature points.

Abstract: An information processing apparatus includes an image input unit which inputs image data containing a face, a face position detection unit which detects, from the image data, the position of a specific part of the face, and a facial expression recognition unit which detects a feature point of the face from the image data on the basis of the detected position of the specific part and determines facial expression of the face on the basis of the detected feature point. The feature point is detected at a detection accuracy higher than detection of the position of the specific part. Detection of the position of the specific part is robust to a variation in the detection target.

Abstract: An image including a face is input (S201), a plurality of local features are detected from the input image, a region of a face in the image is specified using the plurality of detected local features (S202), and an expression of the face is determined on the basis of differences between the detection results of the local features in the region of the face and detection results which are calculated in advance as references for respective local features in the region of the face (S204).

Abstract: A situation monitoring device which enables monitoring of a variety of situations and reporting in response to the situation using a single device is provided. The situation monitoring device is easy to install and to use and a system therefore can be implemented inexpensively. The situation monitoring device recognizes a place or installation where the device is installed (step S102), holds relational information correlating the place of installation and the situation to be recognized and determines a predetermined situation to be recognized according to place of installation recognition results and the relational information (step S104), recognizes a determined predetermined situation (step S106), and reports the result of the predetermined situation to a user (step S108).

Abstract: An information processing apparatus includes an image input unit which inputs image data containing a face, a face position detection unit which detects, from the image data, the position of a specific part of the face, and a facial expression recognition unit which detects a feature point of the face from the image data on the basis of the detected position of the specific part and determines facial expression of the face on the basis of the detected feature point. The feature point is detected at a detection accuracy higher than detection of the position of the specific part. Detection of the position of the specific part is robust to a variation in the detection target.

Abstract: An image-processing apparatus for executing accurate facial expression recognition even for a subject hard to recognize a facial expression is provided. A person's face region is extracted from an image input from an image input unit. A predetermined partial region that changes between when the facial expression is in the first and second states is extracted from the extracted face region. A facial expression evaluation value is calculated using an evaluation value calculation formula. When the calculated facial expression evaluation value exceeds a threshold value, it is determined that the facial expression is in the second state. If the difference between the maximum value and the minimum value of the calculated facial expression evaluation value within a predetermined time is smaller than a predetermined value, the evaluation value calculation formula or its parameter is changed to increase the difference.

Abstract: In a pattern identification method in which input data is classified into predetermined classes by sequentially executing a combination of a plurality of classification processes, at least one of the classification processes includes a mapping step of mapping the input data in an N (N?2) dimensional feature space as corresponding points, a determination step of determining whether or not to execute the next classification process based on the corresponding points, and selecting step of selecting a classification process to be executed next based on the corresponding points when it is determined in the determination step that the next classification process should be executed.

Abstract: An image including a face is input (S201), a plurality of local features are detected from the input image, a region of a face in the image is specified using the plurality of detected local features (S202), and an expression of the face is determined on the basis of differences between the detection results of the local features in the region of the face and detection results which are calculated in advance as references for respective local features in the region of the face (S204).

Abstract: Ignition sections are provided at two locations on each of lower portions of the side surfaces on both sides of a film-forming chamber so as to be provided at four locations in total. A flowing current is applied to the ignition sections when a flammable by-product is ignited. A first detecting section for measuring a pressure in the film-forming chamber is formed on the side surface of the film-forming chamber. A second detecting section is formed at the lower portion of the side surface of the film-forming chamber. A third detecting section for measuring a spatial temperature in the film-forming chamber is formed at an upper portion of the film-forming chamber.

Abstract: A feature point detection unit (153) and feature amount extraction unit (154) extract a plurality of features of an object from input image data. When there are unextracted features of the plurality of features, a weight setting unit (155) sets weights for the extracted features. A facial expression determination unit (156) executes recognition processing of the object based on the features weighted by the weight setting unit (155).