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WiMi Hologram Cloud Developed A Convolutional Neural Network-based Face Recognition Algorithm

 WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider, announced the development of a face recognition algorithm system based on convolutional neural networks. It uses the gray value features of face pixels to extract all feature information in the image with its powerful learning capability, thus avoiding information loss. The system uses a combination of three methods, namely local perceptual field, weight sharing, and pooling, to improve the algorithm’s performance and reduce the order of magnitude of the weight parameters, reducing the complexity of training the convolutional neural network model. The system uses multiple convolutional and pooling layers to extract and reduce the dimensionality of the input image to obtain a high-dimensional feature representation of the image. For face images, the convolutional neural network can learn the features such as contour, texture, and color of the face, thus improving the accuracy and efficiency of face recognition.

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WiMi’s face recognition algorithm system automatically learns the high-level features of the image, improving the accuracy and robustness of face recognition. Its training is performed end-to-end, from the original image to extract features gradually and finally outputting classification results. This training method can automatically learn the relationship between features and perturb and transform the training data by data augmentation, thus increasing the diversity and amount of data, improving the robustness of the face algorithm, and applying it to face recognition tasks of different scales and complexity. In addition, the system uses hardware acceleration such as GPU to achieve high recognition speed and meet the recognition real-time requirements.

Face recognition technology is an advanced recognition technology with unique nature and prominent position in biometric technology, which can play an efficient role in more and more fields. Compared with other recognition methods, the face recognition method is relatively friendly, and recognition is fast and accurate, and with the continuous development of computer technology, face recognition technology is also becoming more and more mature.

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In face recognition technology, the accuracy of recognition is one of the core measures, and the complexity of the shooting environment, such as changes in facial expression, posture, and visibility, makes face recognition technology encounter various challenges in the practical application process.

The convolutional neural network-based face recognition algorithm has become one of the most effective face recognition technologies and is widely used in medical, retail, financial, and other industries. It is foreseeable that WiMi’s face recognition algorithm should have a vast market prospect and huge development potential in the future.

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