Abstract: A facial image generating method, a facial image generating apparatus, and a facial image generating device are proposed. The method comprises: linking an M-dimensional facial feature vector with an N-dimensional demanded feature vector to generate a synthesized feature vector; and generating a synthesized facial image by use of a deep convolutional network for facial image generation and based on the synthesized feature vector. The method further comprises: generating a demand satisfaction score based on the synthesized facial image and the demanded feature vector by use of a deep convolutional network for demand determination; and updating parameters of the deep convolutional network for facial image generation and the deep convolutional network for demand determination based on the demand satisfaction score. A facial image is generated based on a facial feature vector and a demanded feature vector, a facial image with a specific feature can be generated without using the three-dimensional model.

Abstract: Provided is a facial feature adding method, a facial feature adding apparatus, and a facial feature adding device. The facial feature adding method comprises: generating an image to be superimposed based on a given facial image and a feature to be added on the given facial image; and superimposing the image to be superimposed and the given facial image to generate a synthesized facial image. In addition, the facial feature adding method further comprises: generating a first face satisfaction score and a second face satisfaction score by use of a deep convolutional network for face determination and based on the synthesized facial image and a real image with the feature to be added; calculating an L1 norm of the image to be superimposed; and updating parameters of networks based on the first face satisfaction score, the second face satisfaction score, and the L1 norm.

Abstract: A method for face recognition in the video comprises: performing feature extraction on a target face in multiple image frames in the video to generate multiple face feature vectors respectively corresponding to the target face in the multiple image frames; performing time sequence feature extraction on the plurality of face feature vectors to convert the plurality of face feature vectors into a feature vector of a predetermined dimension; and judging the feature vector of the predetermined dimension by using a classifier so as to recognize the target face.

Abstract: Provided is a facial feature adding method, a facial feature adding apparatus, and a facial feature adding device. The facial feature adding method comprises: generating an image to be superimposed based on a given facial image and a feature to be added on the given facial image; and superimposing the image to be superimposed and the given facial image to generate a synthesized facial image. In addition, the facial feature adding method further comprises: generating a first face satisfaction score and a second face satisfaction score by use of a deep convolutional network for face determination and based on the synthesized facial image and a real image with the feature to be added; calculating an L1 norm of the image to be superimposed; and updating parameters of networks based on the first face satisfaction score, the second face satisfaction score, and the L1 norm.

Abstract: A facial image generating method, a facial image generating apparatus, and a facial image generating device are proposed. The method comprises: linking an M-dimensional facial feature vector with an N-dimensional demanded feature vector to generate a synthesized feature vector; and generating a synthesized facial image by use of a deep convolutional network for facial image generation and based on the synthesized feature vector. The method further comprises: generating a demand satisfaction score based on the synthesized facial image and the demanded feature vector by use of a deep convolutional network for demand determination; and updating parameters of the deep convolutional network for facial image generation and the deep convolutional network for demand determination based on the demand satisfaction score. A facial image is generated based on a facial feature vector and a demanded feature vector, a facial image with a specific feature can be generated without using the three-dimensional model.

Abstract: A method for face recognition in the video comprises: performing feature extraction on a target face in multiple image frames in the video to generate multiple face feature vectors respectively corresponding to the target face in the multiple image frames; performing time sequence feature extraction on the plurality of face feature vectors to convert the plurality of face feature vectors into a feature vector of a predetermined dimension; and judging the feature vector of the predetermined dimension by using a classifier so as to recognize the target face.

Abstract: An access control system and a data processing method for the access control system are provided. The access control system includes: a collection device set corresponding to the access passage, for collecting a real-time information data of a target object; an identification device for identifying the target object based on the collected real-time information data and an user registration information of the access control system, and obtaining an identification result; a system control device for carrying out decision logic based on the identification result to generate a control instruction; an access operation device for controlling an operation of the access passage based on the control instruction generated by the system control device.

Abstract: Face hallucination using a bi-channel deep convolutional neural network (BCNN), which can adaptively fuse two channels of information. In one example, the BCNN is implemented to extract high level features from an input image. The extracted high level features are combined with low level details in the input image to produce the higher resolution image. Preferably, a proper coefficient is obtained to adaptively combine the high level features and the low level details.

Abstract: The present invention overcomes the limitations of the prior art by performing facial landmark localization in a coarse-to-fine manner with a cascade of neural network levels, and enforcing geometric constraints for each of the neural network levels. In one approach, the neural network levels may be implemented with deep convolutional neural network. One aspect concerns a system for localizing landmarks on face images. The system includes an input for receiving a face image, and an output for presenting landmarks identified by the system. Neural network levels are coupled in a cascade from the input to the output for the system. Each neural network level produces an estimate of landmarks. The estimate of landmarks is more refined than an estimate of landmark of a previous neural network level.

Abstract: Face hallucination using a bi-channel deep convolutional neural network (BCNN), which can adaptively fuse two channels of information. In one example, the BCNN is implemented to extract high level features from an input image. The extracted high level features are combined with low level details in the input image to produce the higher resolution image. Preferably, a proper coefficient is obtained to adaptively combine the high level features and the low level details.

Abstract: The present invention overcomes the limitations of the prior art by performing facial landmark localization in a coarse-to-fine manner with a cascade of neural network levels, and enforcing geometric constraints for each of the neural network levels. In one approach, the neural network levels may be implemented with deep convolutional neural network. One aspect concerns a system for localizing landmarks on face images. The system includes an input for receiving a face image, and an output for presenting landmarks identified by the system. Neural network levels are coupled in a cascade from the input to the output for the system. Each neural network level produces an estimate of landmarks. The estimate of landmarks is more refined than an estimate of landmark of a previous neural network level.