The application discloses a face recognition method and system The method includes the following steps: S1, acquiring and preprocessing an image; S2, extracting a face feature; S3, determining the distance between the face feature and the standard face feature of a preset standard face and obtaining a determination result; and S4, determining that the face feature and the standard face feature denote the same face when the determination result is higher than a preset threshold, and determining that the face feature and the standard face feature denote different faces when a second determination result is lower than the threshold, wherein in the step S2, the face feature is extracted by a deep convolutional neural network (DCNN), sixteen convolution layers are disposed in the full convolution network, the convolution kernel of each convolution layer has sizes of 1*1 and 3*3, all the convolution layer except the last convolution layer are normalized by a batch normalization layer and are activated by a RELU activation function after the normalization, and a combination of a softmax loss function and a center loss function are used for training Since a DCNN model does not include afull connection layer and consists of 1*1 and 3*3 convolution layers, network parameters and calculated amount can be significantly reduced, and a final model is less than 2M

Face recognition method and system

The invention discloses a face detection and recognition device and method, and the system comprises a face detection module which carries out the face detection of an obtained video frame based on atensorflow neural network frame through an MTCNN (multitask convolution neural network) method; a feature extraction module which carries out the feature extraction of the face data detected by the face detection module based on the tensorflow neural network frame through a FaceNet method; and a classification module which is used for classifying the face features obtained by the feature extraction module so as to achieve the face recognition. According to the above technical scheme, the device achieves the real-time face detection and recognition, is high in accuracy, is quick in processing speed, and is easy for updating in future.

Face detection and recognition device and method

The invention provides a human face identification method and apparatus, computer equipment and a readable storage medium. The method comprises the following steps: any human face image in a trainingset is input into a depth residual error network module group for training operation, a human force identification model is built; human face identification characteristics with specified dimensions are output, corresponding categories of multiple tasks for a corresponding human face image are predicted according to the human face identification characteristics with the specified dimensions basedon a full link layer classifier; a training loss value is calculated according to the predicted corresponding categories of multiple tasks for the corresponding human face image based on a loss function; whether to input any human face image in the training set into the depth residual error network module group for the training operation is determined according to the training loss value, and thehuman force identification model is built. Via the human face identification method and apparatus, the computer equipment and the readable storage medium disclosed in a technical solution of the invention, in-depth of training and construction of the human face identification model can be realized; multiple tasks can be subjected to the training operation, the human face identification model obtained based on the training operation is high in identification effects, multiple tasks of human face identification can be performed, and identification efficiency can be improved.

Human face identification method and apparatus, computer equipment and readable storage medium

The invention proposes an age recognition method for a face image, and the method comprises the steps: obtaining a first real-time image captured by a camera device, and extracting a face region fromthe real-time image through a face recognition algorithm; inputting the face region into a predetermined age recognition model, and extracting an n-dimensional feature vector Y[y0, y1, ..., yi, ..., yn], wherein i is within the section [0, n], and n is not less than zero and not greater than 100; inputting the n-dimensional feature vector Y[y0, y1, ..., yi, ..., yn] into an age recognition formula, and recognizing the age of a face in the real-time image. The invention also proposes an electronic device and a computer readable storage medium. According to the invention, the feature vector reflecting the features of faces at different ages in the face image through the age recognition model, and the age of the face in the face image is recognized according to the feature vector.

Age recognition method and device for face image, and storage medium

The embodiment of the invention discloses an image processing method and system and a server, and the method comprises the steps: acquiring a to-be-processed face image; inputting the face image intoa preset convolutional neural network model, and acquiring the face key point coordinates outputted by the convolutional neural network model in response to the input of the face image, wherein the convolutional neural network model includes a concentration mechanism to enable the convolutional neural network to extract local information including key points in the feature; carrying out the imagecontent understanding of the face image according to the features of the face key points. The convolutional neural network model is provided with the concentration mechanism, thereby enabling the convolutional neural network model to concentrate on the features of the face key points that a user concentrate on in the face image during the feature extraction of the face image, enabling the featuresextracted by the convolutional neural network model to features of the face key points or just comprise the features of the face key points, improving the proportion of the features of the key points, and effectively improving the detection accuracy of the key points in the face image.

Image processing method and system, and server

The invention provides a multi-task deep learning network-based training method, a multi-task deep learning network-based training system, a multi-task deep learning network-based identification method and a multi-task deep learning network-based identification system. The training method includes the following steps that: the face region of a face image in a training set is obtained; key point detection is performed on the face region, so that key feature point positions are obtained; affine transformation is performed on the face image according to the key feature positions, so that an aligned face image can be obtained; and the aligned face image is inputted into a multi-task deep learning network, so that training can be carried out, and therefore, a multi-task deep learning network model can be obtained. The identification method includes the following steps that: affine transformation is performed on a face image to be identified according to the key feature positions of the face image to be identified, so that an aligned face image can be obtained; the aligned face image is inputted into a trained multi-task deep learning network model, so that feature extraction can be carried out, and feature information can be obtained; and the feature information of the face image to be identified is matched with feature information corresponding to each face image in a registration set, so that identification results can be obtained. With the methods and systems adopted, the training and identification efficiency of the multi-task deep learning network can be improved.

Multi-task deep learning network-based training method, system, multi-task deep learning network-based identification method and system

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Multi-task deep learning network-based training method, system, multi-task deep learning network-based identification method and system