159 results about "Face analysis" patented technology

An apparatus and method for image-based 3D photorealistic head modeling are provided. The method for creating a 3D photorealistic head model includes: detecting frontal and profile features in input frontal and profile images; generating a 3D head model by fitting a 3D genetic model using the detected facial features; generating a realistic texture from the input frontal and profile images; and mapping the texture onto the 3D head model. In the apparatus and method, data obtained using a relatively cheap device, such as a digital camera, can be processed in an automated manner, and satisfactory results can be obtained even from imperfect input data. In other words, facial features can be extracted in an automated manner, and a robust “human-quality” face analysis algorithm is used.

An apparatus and method for image-based 3D photorealistic head modeling are provided. The method for creating a 3D photorealistic head model includes: detecting frontal and profile features in input frontal and profile images; generating a 3D head model by fitting a 3D genetic model using the detected facial features; generating a realistic texture from the input frontal and profile images; and mapping the texture onto the 3D head model. In the apparatus and method, data obtained using a relatively cheap device, such as a digital camera, can be processed in an automated manner, and satisfactory results can be obtained even from imperfect input data. In other words, facial features can be extracted in an automated manner, and a robust “human-quality” face analysis algorithm is used.

The invention discloses a face analysis method and a face analysis system integrating a multi-task combined multi-scale convolution neural network, Firstly, the key region search algorithm is used toextract K face regions of interest with different scales from a picture of N *N size, which is used as the input of three channels of multi-scale CNN. Then, CNN is used to extract the features of theK regions of interest to obtain the features of faces with different scales, and the extracted features of faces with different scales are fused in a cascade manner to obtain the fused feature expression; Then, the loss functions of a plurality of tasks are fused to obtain a joint loss function, and the feature expression is used as a learning input to obtain an optimal solution of the joint lossfunction, so as to obtain a processing result of the plurality of tasks. The invention utilizes the correlation between tasks to promote each other, and improves the prediction accuracy rate of a single task.

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.

The invention discloses a public bus passenger flow volume detection system and method based on face identification and position positioning. The Public transportation passenger flow volume detection system comprises an image collection module, a public bus position collection module, a data mining center and data analysis center which are successively connected; the image collection is used for collecting human faces of people getting on and off the public bus; the public bus position collection module is used for recording the driving course of the public bus; the data mining center is used for performing human face analysis and position analysis on the collected image; the data analysis center is used for performing flow statistic analysis according to the result obtained by the data mining center and providing the decision basis to the public bus route planning, operation management, route adjustment, etc. The pblic transportation passenger flow volume detection system and method not only improve the public bus passenger flow volume accuracy, improvethe public bus passenger flow volume detection frequency and greatly saves the flow investigation cost.

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.

The invention provides a similarity discrimination system based on the five sense organs of a human face. An initialization module is used for establishing an initial human face image model. A five-sense-organ positioning module is used for respectively acquiring the positions of five sense organs in the human face region of a plurality of to-be-detected human face images, dividing and extracting the region of each sense organ in the human face region. A feature extraction module is used for obtaining the similarity information of each sense organ in the human face region of the to-be-detected human face images so as to form five-sense-organ feature vectors. A judging module is used for comparing the five-sense-organ feature vectors of a to-be-detected human face with five-sense-organ feature vectors of already known human faces so as to obtain the similarity of the to-be-detected human face. Based on the system, the face features of people are basically classified through judging the similarity of the five sense organs of a human face. Meanwhile, the accurate and detailed analysis and comparison for face features is conducted. As a result, the similarity degree between the sense organs of different people or between the sense organs of people in different images can be provided for users. Furthermore, the system comprehensively embodies the similarity degree between human faces, and improves the human face analysis accuracy and the human face analysis comprehensiveness.

A deep learning framework jointly optimizes the compactness and discriminative ability of face representations. The compact representation can be as compact as 32 bits and still produce highly discriminative performance. In another aspect, based on the extreme compactness, traditional face analysis tasks (e.g. gender analysis) can be effectively solved by a Look-Up-Table approach given a large-scale face data set.