Sold by: Seamfix
Deployed on AWS
Introduce Facial Recognition to your application in minutes with Facial Recognition API by Seamfix.
Overview
Facial Recognition API by Seamfix helps you integrate Facial Recognition to your mobile and web applications, reducing the need for additional infrastructure. You can detect & compare human faces, and Identify people based on the parameters you set. Now you can enjoy accurate recognition suitable for identity verification, security systems and access control.
Highlights
- Level Up Your Facial Recognition: Facial Recognition API by Seamfix accurately compares and matches human faces within seconds.
- Enjoy Accuracy: Facial Recognition API by Seamfix relies on advanced AI technology to ensure accuracy of results.
- Ensure Improved Image Quality: Facial Recognition API by Seamfix recognizes faces from various angles and lighting conditions, so only accurate images are approved.
Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost |
|---|---|---|
ml.m4.xlarge Inference (Batch) Recommended | Model inference on the ml.m4.xlarge instance type, batch mode | $0.95/host/hour |
inference.count.m.i.c Inference Pricing | inference.count.m.i.c Inference Pricing | $0.95/request |
Dimensions summary
You pay based on usage across two independent dimensions. The first charges by the hour for model inference running on an ml.m4.xlarge instance in batch mode. This suits jobs where you process many images together rather than one at a time. The second charges per inference request, so cost rises with the number of facial recognition calls you make. You can use either dimension based on how you run the workload. Batch inference bills by compute time, while request-based inference bills by transaction volume.
Top-of-mind questions for buyers
One request is a single facial recognition call sent to the API. Each face-matching or verification call you make counts once. Cost rises directly with the number of calls. This dimension bills by transaction volume rather than by compute time, so more calls mean a higher bill.
The batch dimension bills by the hour the ml.m4.xlarge instance runs. Charges accrue only while the instance is active and processing. A fully stopped instance does not accrue software charges. Underlying AWS storage fees may still apply separately, but the software meters running time only.
The batch dimension suits jobs where you process many images together, billing by ml.m4.xlarge compute time. The request dimension suits one-at-a-time or on-demand calls, billing per inference. You pick based on your workload. Each dimension bills independently, so you use the one matching how you run the job.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
our facematch model utilizes the state-of-the-art ArcFace algorithm to extract embeddings and compute similarity scores for face recognition. The model is trained on a large dataset of face images to learn the optimal representation of face features, and it can be used in real-time for accurate face matching
Additional details
Inputs
- Summary
The input format is json expecting the source image and target images in base64 string. The source and target images are the innput images you send to the model for matching
- Limitations for input type
- 6MB
- Input MIME type
- application/json
{"source_image": source_img_base64,"target_image": target_img_base64}
https://github.com/seamfix/seamfix-AWSmarketplace/tree/main/data/input/batch/facematch
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
source_image | source_image: source image to be matched with 2nd. image
target_image: target image to be matched with source image | Type: FreeText | Yes |
target_image | source_image: source image to be matched with 2nd. image
target_image: target image to be matched with source image | Type: FreeText | Yes |
Resources
Support
Vendor support
Arena Business Centre, 100 Berkshire Place, Wharfedale Road, Winnersh, RG41 5RD
+44 7756 238056 support@seamfix.com
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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