Pose-invariant face recognition with multitask cascade networks

A Symmetrical Siamese Network Framework With Contrastive Learning for Pose-Robust Face Recognition

Face recognition has achieved remarkable success owing to the development of deep learning. However, most of existing face recognition models perform poorly against pose variations. We argue that, it is primarily caused by pose-based long-tailed data - imbalanced distribution of training samples between profile faces and near-frontal faces. Additionally, self-occlusion and nonlinear warping of facial textures caused by large pose variations also increase the difficulty in learning discriminative features of profile faces. In this study, we propose a novel framework called Symmetrical Siamese Network (SSN), which can simultaneously overcome the limitation of pose-based long-tailed data and pose-invariant features learning. Specifically, two sub-modules are proposed in the SSN, i.e., Feature-Consistence Learning sub-Net (FCLN) and Identity-Consistence Learning sub-Net (ICLN). For FCLN, the inputs are all face images on training dataset. Inspired by the contrastive learning, we simulate pose variations of faces and constrain the model to focus on the consistent areas between the original face image and its corresponding virtual pose face images. For ICLN, only profile images are used as inputs, and we propose to adopt Identity Consistence Loss to minimize the intra-class feature variation across different poses. The collaborative learning of two sub-modules guarantees that the parameters of network are updated in a relatively equal probability between near-frontal face images and profile images, so that the pose-based long-tailed problem can be effectively addressed. The proposed SSN shows comparable results over the state-of-the-art methods on several public datasets. In this study, LightCNN is selected as the backbone of SSN, and existing popular networks also can be used into our framework for pose-robust face recognition.

Application of an Artificial Intelligence System Recognition Based on the Deep Neural Network Algorithm

With the development of artificial intelligence and computer technology, the deep neural network algorithm is applied to the intelligentization of various fields of production and life. However, from the current application status, the application of artificial intelligence technology has many shortcomings. Based on this, this paper starts with the deep neural network algorithm, takes face recognition as the research tool, and deeply studies how to use the deep neural network algorithm to demonstrate the application of intelligent face recognition in complex environments. A face recognition neural network algorithm is proposed, and the accuracy of the algorithm is checked by testing. The results show that the average accuracy of a single sample in the LFW dataset is 99.17%, and the efficiency of using a single sample is close to that of many smelting models, which can be applied to various intelligent recognition scenarios.