Google Cloud Vision AI
Derive insights from your images in the cloud or at the edge with AutoML Vision or use pre-trained Vision API models to detect emotion, understand text, and more. Google Cloud offers two computer vision products that use machine learning to help you understand your images with industry-leading prediction accuracy. Automate the training of your own custom machine learning models. Simply upload images and train custom image models with AutoML Vision’s easy-to-use graphical interface; optimize your models for accuracy, latency, and size; and export them to your application in the cloud, or to an array of devices at the edge. Google Cloud’s Vision API offers powerful pre-trained machine learning models through REST and RPC APIs. Assign labels to images and quickly classify them into millions of predefined categories. Detect objects and faces, read printed and handwritten text, and build valuable metadata into your image catalog.
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Betaface
We offer ready components, such as face recognition SDKs, as well as custom software development services and hosted web services with a focus on image and video analysis, faces and objects recognition. Our technology is used by video and images archives, web advertising and entertainment projects, media content producers, video surveillance and security software solutions, end user and b2b software developers and others. Betaface facial recognition suite embraces whole range of complex operations from fundamental face detection through face recognition (identification, verification or 1:1, 1:N matching) to biometric measurements, face analysis, face and facial features tracking on video, age, gender, ethnicity and emotion recognition, skin, hair and clothes color detection, hairstyle shape analysis and facial features shape description. Our technology is used by video and images archives, web advertising and entertainment projects.
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Azure Face API
Embed facial recognition into your apps for a seamless and highly secured user experience. No machine learning expertise is required. Features include: face detection that perceives faces and attributes in an image; person identification that matches an individual in your private repository of up to 1 million people; perceived emotion recognition that detects a range of facial expressions like happiness, contempt, neutrality, and fear; and recognition and grouping of similar faces in images. Recognize faces according to diverse attributes. Add facial recognition to your apps, all through a single API call. Run Face in the cloud or on the edge in containers. Rely on enterprise-grade security and privacy applied to both your data and any trained models. Detect, identify, and analyze faces in images and videos. Build on top of this technology to support various scenarios. Detect one or more human faces along with attributes.
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Amazon Rekognition
Amazon Rekognition makes it easy to add image and video analysis to your applications using proven, highly scalable, deep learning technology that requires no machine learning expertise to use. With Amazon Rekognition, you can identify objects, people, text, scenes, and activities in images and videos, as well as detect any inappropriate content. Amazon Rekognition also provides highly accurate facial analysis and facial search capabilities that you can use to detect, analyze, and compare faces for a wide variety of user verification, people counting, and public safety use cases.
With Amazon Rekognition Custom Labels, you can identify the objects and scenes in images that are specific to your business needs. For example, you can build a model to classify specific machine parts on your assembly line or to detect unhealthy plants. Amazon Rekognition Custom Labels takes care of the heavy lifting of model development for you, so no machine learning experience is required.
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