The science behind visual ID

A new opt-in feature for Echo Show and Astro provides more-personalized content and experiences for customers who choose to enroll.

With every feature and device we build, we challenge ourselves to think about how we can create an immersive, personalized, and proactive experience for our customers. Often, our devices are used by multiple people in our homes, and yet there are times when you want a more personalized experience. That was the inspiration for visual ID. 

On the all-new Echo Show 15, Echo Show 8, and Echo Show 10, you and other members of your household will soon be able to enroll in visual ID, so that at a glance you can see personalized content such as calendars and reminders, recently played music, and notes for you. 

And with Astro, a new kind of household robot, enrolling in visual ID enables Astro to do things like find you to deliver something, such as a reminder or an item in Astro’s cargo bin.

Creating your visual ID

Visual ID is opt-in, so you must first enroll in the feature, much as you can enroll in voice ID (formerly Alexa voice profile) today. During enrollment, you will use the camera on your supported Echo Show device or Astro to take a series of headshots at different angles. For visual ID to accurately recognize you, we require five different angles of your face. 

During the enrollment process, the device runs algorithms to ensure that each of the images is of high enough quality. For example, if the room is too dark, you will see on-screen instructions to adjust the lighting and try again. You will also see on-screen notifications as an image of each pose is successfully captured. 

The images are used to create numeric representations of your facial characteristics. Called vectors (one for each angle of your face), these numeric representations are just that: a string of numbers. The images are also used to revise the vectors in the event of periodic updates to the visual ID model — meaning customers are not required to re-enroll in visual ID every time there is a model update. These images and vectors are securely stored on-device, not in Amazon’s cloud.

Up to 10 members of a household per account can enroll on each compatible Echo Show or Astro to enjoy more-personalized experiences for themselves. Customers with more than one visual-ID-compatible device will need to enroll on each device individually.

enrollment image_resized.png
A screenshot of the enrollment process, during which the device’s camera takes a series of headshots at different angles.

Identifying an enrolled individual

Once you’ve enrolled in visual ID, your device attempts to match people who walk into the camera’s field of view with the visual IDs of enrolled household members. There are two steps to this process, facial detection and facial recognition, and both are done through local processing using machine learning models called convolutional neural networks. 

To recognize a person, the device first uses a convolutional neural network to detect when a face appears in the camera’s field of view. If a person whom the device does not recognize as enrolled in visual ID walks into the camera’s field of view, the device will determine that there are no matches to the stored vectors. The device does not retain images or vectors from unenrolled individuals after processing. All of this happens in fractions of a second and is done securely on-device.

When your supported Echo Show device recognizes you, your avatar and a personalized greeting will appear in the upper right of the screen.

Echo Show 15_Visual ID.jpg
An example of what Echo Show 15 might show on its screen once an enrolled individual is recognized.

What shows on Astro’s screen will depend on what Astro is doing. For example, if you’ve enrolled in visual ID, and Astro is trying to find you, Astro will display text on its screen — “Looking for [Bob]”, followed by “Found [Bob]” — to acknowledge that it’s recognized you.

Looking for Bob.png
Astro will display text on its screen — “Looking for [Bob]”, followed by “Found [Bob]” — to acknowledge that it’s recognized you.

Enhancing fairness 

We set a high bar for equity when it came to designing visual ID. To clear that bar, our scientists and engineers built and refined our visual ID models using millions of images — collected in studies with participants’ consent — explicitly representing a diversity of gender, ethnicity, skin tone, age, ability, and other factors. We then set performance targets to ensure the visual ID feature performed well across groups.

In addition to consulting with several Amazon Scholars who specialize in computer vision, we also consulted with an external expert in algorithmic bias, Ayanna Howard, dean of the Ohio State University College of Engineering, to review the steps we took to enhance the fairness of the feature. We’ve implemented feedback from our Scholars and Dr. Howard, and we will solicit and listen to customer feedback and make improvements to ensure the feature continues to improve on behalf of our customers.

Privacy by design

As with all of our products and services, privacy was foundational to how we built and designed visual ID. As mentioned above, the visual IDs of enrolled household members are securely stored on-device, and both Astro and Echo Show devices use local processing to recognize enrolled customers. You can delete your visual ID from individual devices on which you’ve enrolled through on-device settings and, for Echo Show, through the Alexa app. This will delete the stored enrollment images and associated vectors from your device. We will also automatically delete your visual ID from individual devices if your face is not recognized by that device for 18 months.

It’s still day one for visual ID, Echo Show, and Astro. We look forward to hearing how our customers use visual ID to personalize their experiences with our devices.

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Shape the Future of Cloud Computing Are you a graduate student passionate about Automated Reasoning and its real-world applications? Join our team of innovators and embark on a journey to revolutionize cloud computing through cutting-edge automated reasoning techniques.Our tools are called billions of times daily, powering the backbone of Amazon's products and services. We are changing the way computer systems are developed and operated, raising the bar for security, durability, availability, and quality. As an Applied Science Intern, you'll have the opportunity to work alongside our brilliant scientists and contribute to groundbreaking projects. From distributed proof search and SAT/SMT solvers to program analysis, synthesis, and verification, you'll tackle complex challenges at the intersection of theory and practice, driving innovation and delivering tangible value to our customers. This internship is not just about executing tasks – you'll explore novel approaches to solving intricate automated reasoning problems. You'll dive deep into cutting-edge research, leveraging your expertise to develop innovative solutions. You'll work on deploying your solutions into production, witnessing the real-world impact of your contributions. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment. Join us and be part of a team that is shaping the future of cloud computing through the power of Automated Reasoning. Apply now and unlock your potential! Amazon has positions available for Automated Reasoning Applied Science Internships in, but not limited to, Arlington, VA; Boston, MA; Cupertino, CA; Minneapolis, MN; New York, NY; Portland, OR; Santa Clara, CA; Seattle, WA; Bellevue, WA; Santa Clara, CA; Sunnyvale, CA. Key job responsibilities We are particularly interested in candidates with expertise in: Theorem Proving, Boolean Satisfiability Solvers, Bounded Model Checking, Deductive Verification, Programming/Scripting Languages, Abstract Interpretation, Automated Reasoning, Static/Program Analysis, Program Synthesis In this role, you will work alongside global experts to develop and implement novel, scalable algorithms and modeling techniques that advance the state-of-the-art in areas at the intersection of Natural Language Processing and Speech Technologies. You will tackle challenging, groundbreaking research problems on production-scale data, with a focus on natural language processing, speech recognition, text-to-speech (TTS), text recognition, question answering, NLP models (e.g., LSTM, transformer-based models), signal processing, information extraction, conversational modeling, audio processing, speaker detection, large language models, multilingual modeling, and more. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment. Key job responsibilities We are particularly interested in candidates with expertise in: Theorem Proving, Boolean Satisfiability Solvers, Bounded Model Checking, Deductive Verification, Programming/Scripting Languages, Abstract Interpretation, Automated Reasoning, Static/Program Analysis, Program Synthesis In this role, you will work alongside global experts to develop and implement novel, scalable algorithms and modeling techniques that advance the state-of-the-art in areas at the intersection of Natural Language Processing and Speech Technologies. You will tackle challenging, groundbreaking research problems on production-scale data, with a focus on natural language processing, speech recognition, text-to-speech (TTS), text recognition, question answering, NLP models (e.g., LSTM, transformer-based models), signal processing, information extraction, conversational modeling, audio processing, speaker detection, large language models, multilingual modeling, and more. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment.
US, WA, Seattle
Unleash Your Potential as an AI Trailblazer At Amazon, we're on a mission to revolutionize the way people discover and access information. Our Applied Science team is at the forefront of this endeavor, pushing the boundaries of recommender systems and information retrieval. We're seeking brilliant minds to join us as interns and contribute to the development of cutting-edge AI solutions that will shape the future of personalized experiences. As an Applied Science Intern focused on Recommender Systems and Information Retrieval in Machine Learning, you'll have the opportunity to work alongside renowned scientists and engineers, tackling complex challenges in areas such as deep learning, natural language processing, and large-scale distributed systems. Your contributions will directly impact the products and services used by millions of Amazon customers worldwide. Imagine a role where you immerse yourself in groundbreaking research, exploring novel machine learning models for product recommendations, personalized search, and information retrieval tasks. You'll leverage natural language processing and information retrieval techniques to unlock insights from vast repositories of unstructured data, fueling the next generation of AI applications. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated. Join us at the forefront of applied science, where your contributions will shape the future of AI and propel humanity forward. Seize this extraordinary opportunity to learn, grow, and leave an indelible mark on the world of technology. Amazon has positions available for Machine Learning Applied Science Internships in, but not limited to Arlington, VA; Bellevue, WA; Boston, MA; New York, NY; Palo Alto, CA; San Diego, CA; Santa Clara, CA; Seattle, WA. Key job responsibilities We are particularly interested in candidates with expertise in: Knowledge Graphs and Extraction, Programming/Scripting Languages, Time Series, Machine Learning, Natural Language Processing, Deep Learning,Neural Networks/GNNs, Large Language Models, Data Structures and Algorithms, Graph Modeling, Collaborative Filtering, Learning to Rank, Recommender Systems In this role, you'll collaborate with brilliant minds to develop innovative frameworks and tools that streamline the lifecycle of machine learning assets, from data to deployed models in areas at the intersection of Knowledge Management within Machine Learning. You will conduct groundbreaking research into emerging best practices and innovations in the field of ML operations, knowledge engineering, and information management, proposing novel approaches that could further enhance Amazon's machine learning capabilities. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment. A day in the life - Design, implement, and experimentally evaluate new recommendation and search algorithms using large-scale datasets - Develop scalable data processing pipelines to ingest, clean, and featurize diverse data sources for model training - Conduct research into the latest advancements in recommender systems, information retrieval, and related machine learning domains - Collaborate with cross-functional teams to integrate your innovative solutions into production systems, impacting millions of Amazon customers worldwide - Communicate your findings through captivating presentations, technical documentation, and potential publications, sharing your knowledge with the global AI community