The invention discloses a people searching method and system based on face and human body identification; the method selects an identification mode from two identification modes through sharpness determinations, thus providing options for the identification modes, and effectively improving the identification performance; the method uses a human body identification mode to carry out after-detection tracking, thus preventing face identification failure caused by an unclear face, realizing face detection and tracking of a target person, improving the city safety level without changing hardware, and saving modification costs; the method uses the human body identification to realize ID authentication and behavior monitoring, thus reducing ID authentication and monitoring failure probability, and improving post-event tracing back capability; the searching system comprises a data determination unit, a mode selection unit, and a human body identification unit; the human body identification unit also comprises a face obtaining module and a behavior detection module; beneficial effects of the method and system are realized

People searching method and system based on face and human body identification

The embodiments of the present invention provide a face recognition system and method. The system is deployed on a cloud platform. The system includes a load balancing server, an application service cluster and a face recognition cluster; the load balancing server is used for selecting a target application server, sending an obtained target face recognition request to the target application server and receiving a result acquisition request sent by the target application server; a target face recognition server is selected, and the result acquisition request is forwarded to the target face recognition server; the target application server is used for receiving the target face recognition request, obtaining information to be identified according to information to be processed carried in the target face recognition request; the result acquisition request is sent to the load balancing server; the target face recognition server is used for receiving the result acquisition request forwarded by the load balancing server; face recognition is performed on a source image; and a face recognition result is sent to the initiator of the result acquisition request. With the face recognition system and method provided by the embodiments of the present invention adopted, face recognition efficiency is improved.

Face recognition system and method

The invention belongs to the field of image recognition, and specifically provides a mobile terminal face recognition method and system based on the technology of embedded deep learning. The invention aims at solving problems in the prior art that a human face recognition program is complex, an occupied memory is large, the recognition speed is small, the recognition precision is low and a hand-held mobile terminal for human face recognition is limited by a network signal. In order to solve the above problems, the system comprises a human face detection module for obtaining a face region of a target person; a feature extraction module which is used for obtaining the face features of the target person according to the obtained face region; and a feature comparison module which is used for calculating the similarity between the obtained face features of the target person and preset face features, wherein the human face detection module, the feature extraction module and the feature comparison module are disposed on a mobile terminal. Therefore, the system enables the mobile terminal to be able to judge the identity of the target person quickly and accurately under the condition that there is no network signal.

Mobile terminal face recognition method and system based on technology of embedded deep learning

The invention provides a method for automatically completing codes on the basis of LSTM (Long Short Term Memory). The method comprises the following steps: performing source code processing, and analyzing source codes by using an abstract grammar tree; performing off-line training, and training a language model by using an LSTM model; performing on-line code completion, and completing codes according to the trained language model. The LSTM model comprises constraint character level LSTM and mark character level LSTM with former context marking character encodes. By adopting the method, automatic code completion can be achieved by inputting any character into any place in the encoding process, recommendation of any code can be also achieved, and the recommendation process accuracy can be also ensured. The technical scheme of the invention has the characteristics of being simple and rapid, and the code recommendation accuracy and the recommendation efficiency can be relatively well improved.

Method for automatically completing code on basis of LSTM (Long Short Term Memory)

The invention discloses face recognition and movement track judging methods under high noise. The face recognition method comprises an image acquisition step, a feature extraction step, a feature comparison step, and a judgment step. In the image acquisition step, images of a video stream are acquired at intervals. In the feature comparison step, extracted features are compared with features stored in a face library, corresponding result vectors and (respectively representing the IDs of most similar k persons in the face library and the similarity thereof) of faces are obtained, and two m*k matrixes are obtained for each image, wherein m is the number of persons in the frame of image. In the judgment step, a candidate person set is obtained according to images decoded from the same video stream and the number of times each ID appears in the comparison results, and a suspect ID is determined according to the number of times each ID in the candidate person set appears in images decoded from different video streams and the similarity. The method is of high detection reliability and low misjudgment rate under the condition of high noise.

Face recognition and movement track judging methods under high noise

The invention relates to an intelligent face tracking system and method based on depth learning and large-scale clustering. The system comprises a video input unit, a distribution server, a face identification server cluster, a streaming media server, a distributed file server, a message center server, a web front server and a client of a common operation system. According to the invention, by use of large-scale cluster servers, based on depth learning based face identification technology, a quite high identification rate can still be maintained under the condition of degraded image quality, and more important, a quite low false alarm rate and a quite low missed examination rate are maintained in a large-scale database, such that the reliability and the robustness of the intelligent tracking system can be ensured, and the intelligent tracking system based on face identification can be really applied to the field of safety protection.

Intelligent face tracking system and method based on depth learning and large-scale clustering

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Lu Yang

Author Tools

Chat about Author

Papers

Intelligent face tracking system and method based on depth learning and large-scale clustering