Abstract: A pyram-shaped deep-sea pressure-resistance shell and a design method therefor. The shell comprises a conical shell, an annular combined shell, a cylindrical shell, a flange bolt, and a perforated thick plate; a bottom end of the conical shell is connected with a top end of the annular combined shell, the conical shell being in communication with an interior part of the annular combined shell; the perforated thick plate blocks the bottom end of the annular combined shell, the perforated thick plate and the annular combined shell being connected by means of multiple flange bolts; the cylindrical shell is disposed inside the annular combined shell, a lower end of the cylindrical shell being inserted in a gap between the annular combined shell and the perforated thick plate.

Abstract: The present disclosure provides a method and apparatus for generating training data for human face recognition, a device and a computer storage medium, wherein the method comprises: inputting accessory-not-worn face images into a generative network, to obtain accessory-worn face images; using the accessory-worn face images as second training data for building the human face recognition model; wherein the generative network is a generative network in a generative adversarial network obtained by pre-training with first training data, the first training data including the accessory-not-worn face images and accessory-worn face images corresponding to a same user identifier. In the present disclosure, the accessory-worn face images obtained in a data augmentation manner greatly expand the amount of training data for building the human face recognition model, and thereby improve the recognition accuracy of the accessory-worn face images.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for outputting information. The method includes: acquiring a first image and a second image, the first image including a first face image region, and the second image including a second face image region; generating an image matrix of the first image and an image matrix of the second image; inputting respectively the image matrix of the first image and the image matrix of the second image into a pre-trained convolutional neural network to obtain a characteristic vector of the first image and a characteristic vector of the second image; calculating a distance between the characteristic vector of the first image and the characteristic vector of the second image; and outputting, based on the calculated distance, information of an object relationship between an object the first face image region belongs and an object the second face image region belongs.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for outputting information. The method comprises: acquiring a first image and a second image, the first image including a first face image region, and the second image including a second face image region; generating an image matrix of the first image and an image matrix of the second image; inputting respectively the image matrix of the first image and the image matrix of the second image into a pre-trained convolutional neural network to obtain a characteristic vector of the first image and a characteristic vector of the second image; calculating a distance between the characteristic vector of the first image and the characteristic vector of the second image; and outputting, based on the calculated distance, information of an object relationship between an object the first face image region belongs and an object the second face image region belongs.

Abstract: The present disclosure provides a method and apparatus for building a human face recognition model, a device and a computer storage medium, wherein the method comprises: regarding a known user's face images annotated with ages as training samples; using the training samples to train a deep neural network to obtain a human face recognition model, the human face recognition model being used to perform user identification for input face images. The present disclosure can solve the problem about reduction of the face recognition rate caused by age changes, and improve robustness of face recognition for ages.

Abstract: The present disclosure provides a method and apparatus for generating training data for human face recognition, a device and a computer storage medium, wherein the method comprises: inputting accessory-not-worn face images into a generative network, to obtain accessory-worn face images; using the accessory-worn face images as second training data for building the human face recognition model; wherein the generative network is a generative network in a generative adversarial network obtained by pre-training with first training data, the first training data including the accessory-not-worn face images and accessory-worn face images corresponding to a same user identifier. In the present disclosure, the accessory-worn face images obtained in a data augmentation manner greatly expand the amount of training data for building the human face recognition model, and thereby improve the recognition accuracy of the accessory-worn face images.