AI Facial Recognition web system
AI Facial Recognition web system
02 Mar,2019
$787.00
Code reviewed by PieceX
$787.00
Code reviewed by PieceX
Facial recognition and facial detection web system in Python and Django.
Ready-to-use code with User Interface for facial recognition and facial detection. Upload images and let the AI find matches for people.
Upload individual or multiple images at the time.
While facial recognition gives an identification or a match between a set of images, facial detection allows identifying if there is a human face (or more) within a picture.
With the following project you will be able to:
- Use it as it is for web facial recognition and facial detection
- Upload single images or multiple images to build a known faces library
- Find a match by facial recognition for a single image or multiple files
- Submit an image for facial detection
- Browse the results as Web client
- Access to the database and source code
- Use the recognition class for other projects
Includes pre-trained data (pre-trained data from public libraries*, you can also use your own training dataset or another public one)
External Sources
The model was trained using the following dataset: https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/. (please check their website for changes in License for the landmarks; additionally, you can use a Tensorflow MIT dataset for training).
For this system, we will use the face_detector to find the faces. Alternatively, you can use your own datasets and replace those files.
How to Use:
First, copy the two .dat files from the "Part2" zip folder inside the Part1 zip folder (where the manage.py file is).
Install Python3 and Django in your server/localhost
Copy the folder in your decided location (or server’s html folder).
In the terminal cd inside the folder with the manage.py file.
Type the followings:
> python3 manage.py makemigrations
> python3 manage.py migrate
> python3 manage.py runserver
Limitations
- The detections correspond to statistical probabilities, hence the accuracy is not 100% guaranteed.
- The image quality and complexity interferes with the accuracy of the prediction.
Use it as it is for web facial recognition and facial detection
Upload single images or multiple images to build a known faces library
Find a match by facial recognition for a single image or multiple files
Submit an image for facial detection
Browse the results as Web client
Access to the database and source code
Use the recognition class for other projects
Includes pre-trained data (pre-trained data from public libraries , you can also use your own training dataset or another public one)
External Sources
The model was trained using the following dataset: https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/. How to Use:
First, copy the two .dat files from the Part2 zip folder inside the Part1 zip folder (where the manage.py file is).

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luckykhemani3471
06 Jun, 2020
do we have demo url?\n
VINEETSHARMA0346
09 May, 2020
hi nwe are intreseted in your AI system
LabAI Seller
21 May, 2020
Hi Thank you for your interest in my product If you have any questions please feel free to ask me here or send me a direct message on my profile
ramesh
23 Apr, 2020
pl send the trial to have experience to negotiate and buy the same n
ramesh
23 Apr, 2020
Hi n
SaumilShah1145
01 Apr, 2020
I am ready to buy at 350 USD How do I go ahead
SaumilShah1145
29 Mar, 2020
Who are you Wahab Shah Why you replied to my comment to the seller
LabAI Seller
01 Apr, 2020
Hello, I am the seller. How may I help you?
Wahab Shah
29 Mar, 2020
Hi sir
SaumilShah1145
25 Feb, 2020
What is the best price you can offer this My budget is not more than 400 USD n
Wahab Shah
29 Mar, 2020
350 usd will be good
radarbruce
13 Sep, 2019
Hi. Does what kind of database does it use? I want to implement something similar but I don‘ t have much knowledge in python...
LabAI Seller
13 Sep, 2019
Right now it includes the structure in SQL Lite, but you can change to another database engine with the connection string. The product is a "ready to go" kind of product, so what you can do is to use it as a standalone and then process the results in the database, all the recognition and information is stored in the database so the integration is easier. You can also modify everything and just use the recognition part as a webservice in python. Please let me know if you have any further questions.