The present invention discloses an image identification method and device, belonging to the image processing field. The method comprises: obtaining a target image which an image to be identified; performing feature extraction of the target image through a presetting convolution neural network model, and obtaining the attribute vectors of the target image, wherein the attribute vectors include the probability of a plurality of presetting word attributes in the target image; and performing semantic analysis processing of the attribute vectors trough the presetting convolution neural network model, and obtaining the descriptive statement of the target image, wherein the descriptive statement is configured to describe the content of the target image. Namely, the image identification method and device can combine the image identification and the natural language processing to realize that the target image is converted to the descriptive statement in a character mode so as to vividly describe the concrete content of the target image in more detail and improve the image identification effect.

Image identification method and device

The invention provides a face detection method and device, and the method comprises the steps: inputting a to-be-detected image into a first convolution neural network, detecting a face region in theto-be-detected image through the first convolution neural network, and marking a first face candidate window in the detected face region; adjusting the scale of an (M-1) face candidate window to be the scale of an M-th convolution neural network, inputting the scale of the M-th convolution neural network into the M-th convolution neural network, detecting a face region of the (M-1) face candidatewindow through the M-th convolution neural network, and carrying out the marking of an M-th face candidate window in the detected face region, wherein M=2, 3, ..., N, and N >=3; merging an N-th face candidate window and an (N-1)-th face candidate window into a face well-chosen window through a global non-maximum suppression method, wherein the first convolution neural network, the second convolution neural network, ..., the N-th convolution neural network are cascaded according to a scale sequence of convolution neural networks from the small to the big. The invention also provides a face detection method and device. The method and device can detect the face region precisely and efficiently.

Face detection method and device

The invention provides a face recognition method based on a convolutional neural network. The face recognition method comprises the following steps: carrying out necessary pretreatment at early stage on a facial image to obtain an ideal facial image; selecting the ideal facial image as the input of the convolutional neural network to enter U0, wherein the output of the U0 enters UG, and the output of the UG is taken as the input of US1; extracting edge components in different directions in an input image as first-time feature extraction and outputting to the input of special UC1 through supervised training by the S nerve cell of the US1; taking the output of the UC1 as the input of US2, completing the second-time feature extraction by the US2 and taking as the input of UC2; taking the output of the UC2 as the input of US3, and completing the third-time feature extraction by the US3 and taking as the input of UC3; taking the output of the UC3 as the input of US4, and obtaining the weight, threshold value and neuron plane number in each layer in a supervision competitive learning mode by the US4 and taking as the input of the UC4; taking the UC4 as the output layer of the network, and outputting the final mode recognition result of the network determined by the maximum output result of the US4. According to the face recognition method, the recognition rate of faces in complex scenarios can be improved.

Face recognition method based on convolutional neural network

Disclosed are a detection method and apparatus The method comprises: inputting a template image and a test image to a Siamese network that has been trained, and separately performing forward computing to obtain eigenvectors of the template image and the test image; calculating a similarity between the eigenvectors of the template image and the test image; and determining, according to the similarity, whether the test image matches the template image Because a Siamese network can extract advanced features of images, in embodiments of the present invention, by inputting the images to a Siamese network that has been trained, it can be accurately determined whether a template image matches a test image, achieving a high accuracy rate of determining

Detection method and apparatus

The invention provides a face comparison method based on high-dimensional LBP (Local Binary Patterns) and convolutional neural network feature fusion. The method comprises the following steps: firstly, two types of face images are input, preprocessing is independently carried out, then, each image independently extracts the high-dimensional LBP features and the CNN (Convolutional Neural Network) features of the image, the two features are combined and are subjected to dimensionality reduction via PCA (Principal Component Analysis), and finally, a Joint Bayesian method is used for obtaining a similarity of the two images. In a feature extraction process, since the high-dimensional LBP extracts local information and the CNN extracts global information, two types of information are fused, and the information extracted by the features is complete. Compared with a method which separately uses the high-dimensional LBP or the CNN, the method is higher in accuracy and better in robustness and achieves a real-time face comparison rate.

Face comparison method based on high-dimensional LBP (Local Binary Patterns) and convolutional neural network feature fusion

The disclosure discloses a face authentication method and device. The face authentication method includes: acquiring multiple face training images; extracting Gabor features of the multiple face training images; extracting Pattern of Oriented Edge Magnitude (POEM) features of the multiple face training images; fusing the Gabor features of the multiple face training image and the POEM features of the multiple face training image to acquire positive samples and negative samples of the multiple face training images; training the positive samples and negative samples of the multiple face training images to obtain training results by an AdaBoost algorithm; and performing face authentication by the training results. By the disclosure, the problem of difficulty of a face authentication method in the related technology in combination of efficiency and recognition rate is solved, and the effects of improving feature extraction efficiency of face recognition and increasing the face recognition rate are further achieved.

Face authentication method and device

The invention discloses methods and devices for face characteristic extraction and authentication. The face characteristic extraction method comprises steps that: two-dimensional Hilbert transformation on an acquired face image is carried out; the face image after two-dimensional Hilbert transformation is expressed in a mode of a two-dimensional parsing signal. Through the methods and the devices, technical problems of poor performance and high complexity existing in a face identification method in the prior art are solved, complexity of system realization is simplified, and accuracy and precision of the system are improved.

Methods and devices for face characteristic extraction and authentication

Disclosed are a facial characteristic extraction and authentication method and device. The facial characteristic extraction method comprises: conducting two-dimensional Hilbert transform on an obtained facial image; representing the transformed facial image by a two-dimensional analytic signal. The present invention solves the problems of poor performance and high complexity of the facial recognition methods in the prior art, simplifies the complexity of system implementation, and improves system precision and accuracy.

Facial characteristic extraction and authentication method and device

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Face authentication method and device

Methods and devices for face characteristic extraction and authentication

Facial characteristic extraction and authentication method and device