Guidelines on face attributes - Amazon Rekognition

FaceDetail - Amazon Rekognition

This is the API Reference for Amazon Rekognition Image, Amazon Rekognition Custom Labels, Amazon Rekognition Stored Video, Amazon Rekognition Streaming Video. It provides descriptions of actions, data types, common parameters, and common errors.

Emotion - Amazon Rekognition

This is the API Reference for Amazon Rekognition Image, Amazon Rekognition Custom Labels, Amazon Rekognition Stored Video, Amazon Rekognition Streaming Video. It provides descriptions of actions, data types, common parameters, and common errors.

Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification

Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. In this work, we present an approach to evaluate bias present in automated facial analysis algorithms and datasets with respect to phenotypic subgroups. Using the dermatologist approved Fitzpatrick Skin Type classification system, we characterize the gender and skin type distribution of two facial analysis benchmarks, IJB-A and Adience. We find that these datasets are overwhelmingly composed of lighter-skinned subjects (79.6% for IJB-A and 86.2% for Adience) and introduce a new facial analysis dataset which is balanced by gender and skin type. We evaluate 3 commercial gender classification systems using our dataset and show that darker-skinned females are the most misclassified group (with error rates of up to 34.7%). The maximum error rate for lighter-skinned males is 0.8%. The substantial disparities in the accuracy of classifying darker females, lighter females, darker males, and lighter males in gender classification systems require urgent attention if commercial companies are to build genuinely fair, transparent and accountable facial analysis algorithms.

Facial age estimation: Using AI to support initial age decisions (A guide) (accessible)

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userinterface.wiki

A living manual for better interfaces. Learn design principles, motion, typography, and more.

Meta's smart glasses companion app ships a complete, dormant face-recognition pipeline on a stock account.

Stella is the companion app for Meta's smart glasses. Inspecting version 273.0.0.21 of the Android build (com.facebook.stella), I found the entire computational and storage stack for on-device facial recognition: three face models, a local database schema, a cosine-similarity vector index dimensioned to match the models,

Models – Hugging Face

Explore machine learning models.

Taking a look at looksmaxxing — and what parents should know

The trend among boys and young men of optimizing their physical appearance includes dangerous practices. Experts offer advice on how to talk to their sons about body image and healthy behaviors.

The Customer Discovery Handbook

This is a practioner's guide to conducting design research on personas, needfinding, value, and usability.

r/SkincareAddiction

The place for all things related to skincare, with a science-based approach.

Maintain the unmaintainable - a Hugging Face Space by transformers-community

This application visualizes how over 400 machine learning models in the Transformers library are connected and related to each other. Users can see which models are derived from or import features ...

moonshotai (Moonshot AI)

Org profile for Moonshot AI on Hugging Face, the AI community building the future.

AI-synthesized faces are indistinguishable from real faces and more trustworthy

Artificial intelligence (AI)–synthesized text, audio, image, and video are being weaponized for the purposes of nonconsensual intimate imagery, financial fraud, and disinformation campaigns. Our evaluation of the photorealism of AI-synthesized faces indicates that synthesis engines have passed through the uncanny valley and are capable of creating faces that are indistinguishable—and more trustworthy—than real faces.