Pushing by big data and deep convolutional neural network (CNN), the performance of face recognition is becoming comparable to human. Using private large scale training datasets, several groups achieve very high performance on LFW, i.e., 97% to 99%. While there are many open source implementations of CNN, none of large scale face dataset is publicly available. The current situation in the field...

In order to solve the problem that face recognition is sensitive to illumination variation and local binary pattern has small spatial support region. A novel face recognition approach which is multi-scale logarithmic difference face recognition based on local binary pattern is proposed. Firstly, LBP operator is used to extract the texture feature of the face. Secondly, the LBP feature is used t...

To address one of the important and challenging problems – large-scale contentbased face image retrieval. Given a query face image, content-based face image retrieval tries to find similar face images from a large image database.Large-scale content-based face image retrieval is an enabling technology for many emerging applications. In this method, to utilize automatically detected human attribu...

objectives: liver cirrhosis can change many aspects of life of the patients and their family and effects society. we aimed to study the utility of cirrhosis from the point of view of the patients, their family, and their care takers to find appropriate interventions, and training and counselling programmes to support patients. methods: in this cross-sectional study with a goal-based sampling me...

In this work, we address the problem of large-scale online face clustering: given a continuous stream unknown faces, create database grouping incoming faces by their identity. The must be updated every time new arrives. addition, solution efficient, accurate and scalable. For purpose, present an gaussian mixture-based clustering method (OGMC). key idea is proposal that identity can represented ...