45 results about "Eigenface" patented technology

A face recognition system and process for identifying a person depicted in an input image and their face pose. This system and process entails locating and extracting face regions belonging to known people from a set of model images, and determining the face pose for each of the face regions extracted. All the extracted face regions are preprocessed by normalizing, cropping, categorizing and finally abstracting them. More specifically, the images are normalized and cropped to show only a persons face, categorized according to the face pose of the depicted person's face by assigning them to one of a series of face pose ranges, and abstracted preferably via an eigenface approach. The preprocessed face images are preferably used to train a neural network ensemble having a first stage made up of a bank of face recognition neural networks each of which is dedicated to a particular pose range, and a second stage constituting a single fusing neural network that is used to combine the outputs from each of the first stage neural networks. Once trained, the input of a face region which has been extracted from an input image and preprocessed (i.e., normalized, cropped and abstracted) will cause just one of the output units of the fusing portion of the neural network ensemble to become active. The active output unit indicates either the identify of the person whose face was extracted from the input image and the associated face pose, or that the identity of the person is unknown to the system.

In blendshape-based facial animation, two main approaches are used to create the key expressions: manual sculpting and statistically-based techniques. Hand-generated expressions have the advantage of being intuitively recognizable, thus allowing animators to use conventional keyframe control. However, they may cover only a fraction of the expression space, resulting in large reproduction animation errors. On the other hand, statistically-based techniques produce eigenfaces that give minimal reproduction errors but are visually non-intuitive. In the invention the applicants propose a technique to convert a given set of hand-generated key expressions into another set of so-called quasi-eigen faces. The resulting expressions resemble the original hand-generated expressions, but have expression space coverages more like those of statistically generated expression bases. The effectiveness of the proposed technique is demonstrated by applying it to hand-generated expressions.

An improved method of watermarking picture identification documents (IDs) such as passports, driver's licenses, identification cards and the like which combines biometric information with digital watermarking to provide an improved secure picture ID document and authentication of same. A facial image—that is part of the identification document—is processed such that particular facial features are extracted from the overall facial image. The extracted features are used to generate a watermark key that is subsequently used to embed a unique watermark into the facial image or other location on the identification document.

A face meta-data creating technique in which the description length is short and which is used to extract the face feature for face recognition immune to the local error. An area cut-out section ( 121 ) cuts out a local area of a face image. Frequency feature extracting means ( 122 ) extracts the frequency spectrum of the local area. Vector projection means ( 123 ) projects the obtained frequency feature onto a partial space to extract the face feature of the local area. Aface meta-data unit ( 12 ) extracts the face features from local areas cut out in different positions, thus creating face features as face meta-data.

The invention provides face recognition module and method. The face recognition module includes a near infrared flash, a master near infrared camera, an artificial intelligence NIR image model, an artificial intelligence original image model, and an artificial intelligence fusion model. The NIR flash flashes near infrared light. The master near infrared camera captures a NIR image. The artificialintelligence NIR image model processes the NIR image to generate NIR features. The artificial intelligence original image model processes a 2 dimensional second camera image to generate face featuresor color features. The artificial intelligence fusion model generates 3 dimensional face features, a depth map and an object's 3 dimensional model according to the NIR features, the face features andthe color features. In the invention, successful rate and optimum extracted features of face recognition are improved, so that the module and method can be used for artificial intelligence face detection, artificial intelligence facial feature generation, artificial intelligence landmark generation, artificial intelligence live detection artificial intelligence depth map generation, etc.

The invention requests to protect a face recognition system and a face recognition method which are based on the second-order two-dimensional principal component analysis, and belongs to the fields of image processing technology, computer vision technology and mode identification technology. The invention provides the face recognition method which has low computational complexity and is based on the second-order two-dimensional principal component analysis. The method researches the face recognition problem in the condition of illumination change, and provides the face recognition method of the second-order two-dimensional principal component analysis. The face recognition method comprises: respectively applying the (2D)<2>PCA technology to an original image matrix set and a residual image matrix set to obtain a first-order feature matrix and a second-order feature matrix, thereby determining reconstructed images of sample images and reconstructed images of residual images; superimposing the two reconstructed images to obtain reconstructed images of original images. The method of the invention has higher recognition accuracy, and save more computing time than the eigenface method and the second-order eigenface method. The method of the invention can be widely used in the image recognition field.