The Good and The Bad of Creating “Look like a Celebrity App”


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Do I look like Eva Mendes? Do I resemble a famous personality? Many times people exclaim that they saw a person that looks like a TV star. Hopefully, now we have applications that will let you know which star you look like. Such apps use face recognition technology.

What if we tell you that you have an app that makes you realize your childhood dreams of looking like a princess or an intergalactic space alien or an Avataar superhero? Celebrity look-alike apps use AI to analyze and provide celebrity replication results. Such apps use machine learning algorithms and facial recognition technology and analyze your facial data to find out which celebrity you resemble.

What is face detection software?

Facial recognition apps/Face detection apps/Face tracking apps have the potential to add a custom visual experience for your app’s users. These are compatible with popular frameworks. You can create a great UGC app with Banuba SDK tools. AR Face filters and AI Video editing.

Who is your celebrity twin? Just open the app, click your picture, submit it and get to know who is your celebrity’s closest match. It’s 100% free. Get to know the fastest comparisons, with improved accuracy. 

Lensa is a new photo creativity app that might offer exactly what you are looking for. This program uses artificial intelligence (AI) software to create avatars using a few of your own image uploads. Not only the general public, but celebrities are already trying to Ai-if their selfies.

Examples of trending 13 face detection and celebrity doppelganger apps

(Almost every app has similar offerings)

  1. Gradient App: Face Beauty Editor
  2. Replika: My AI Friend
  3. Reface: Face swap videos/memes
  4. Celebs: Celebrity Look Alike App
  5. Celebrity Face Morph: Transfo
  6. Look-alike: Celebrity
  7. Facer: you look like a celeb
  8. LikeStar: Face like a celeb
  9. Star by Face: celebs look alike
  10. Y-Star: Celebrities Look Alike
  11. Looky: Celebrity Look Alike
  12. Lookify
  13. Face scanner who do I look like

Features of celebrity face detection apps

  • Recolour hair
  • Blur background
  • Edit Photo
  • Create Collages
  • Retouch Face
  • Beatify Portraits
  • Adjust Skin Tone
  • Apply filters
  • Upload own videos
  • Get faster processing times
  • Social login
  • Photo Editor
  • Celebrity look-alike filter
  • AI Portraits
  • The animal you look like
  • Ethnicity Estimation

Benefits of Celeb Face Recognition App

Apps like gradient have a celebrity facial match feature that analyzes the facial data and finds out your celebrity’s top data. Apps like celebs help recognize your features that match your celebrity twin. It also has features to find twin faces, the best resemblance, and social media sharing. Apps like Star by Facework on facial recognition technology and has an image generator to process image comparisons of you and the celebrity you look alike with. It does not store user data and takes complete responsibility for user security. Apps like the Replika celebrity look alike app have a wide variety of data of celebrities and hence it provides the best results on editing and sharing data on social media. Apps like Y Star recognize facial features, match them within their celebrity database, and provide the best nearby results. Look-alike application  

How does it work?

Facial recognition apps generate highly realistic transformations of human faces in photographs by using neural networks based on AI/ML/Neural networks. Register with the app, and upload at least 10 images of yourself. The more images you upload, the more you’ll have a better chance of getting your closest celebrity look-alike person. The app encourages people to include as many facial features, angles, and expressions to give the best results.

  1. Download the app on your smartphone
  2. Capture/upload the image from the storage
  3. The app analyzes the facial data and matches it with the database
  4. The app generates doppelganger results for saving/sharing

Technology Stack

The facial recognition pipeline comes with a wide product range. Facial recognition has several use cases and here we are going to discuss the various technologies required to create FRT software. 4 primary stages in facial detection include: Detect, align, represent, and verify. And there are many outputs of the representation stage facial images as vectors like VGG Face, Google FaceNet, Dlib, and ArcFace. Facial recognition tasks can be run with a deep face for python with few lines of code.

  • Face verification has O(d) time complexity in big O notation where d is the number of dimensions in the facial vector embedding.
  • Relational databases: Oracle, Microsoft SQL, IBM DB2, SQLite, MySQL, Redis, Cassandra, Hadoop, MongoDB,
  • Libraries: Spotify Annoy, Facebook Faiss, NMSLIB, Elasticsearch, Pinecone,
  • Database: Neo4j
  • Algorithms: k-NN, a-NN

More innovations are possible with Swift, Core ML, TuriCreate, Clearview AI, and Vision API. These accentuate the native face detection API, Face tracking with ARkit, text and barcode recognition, and image registration. It allows the use of custom Core ML models for all sorts of imaging tasks. Dedicated Neural engines, and ML accelerators, enable developers to deploy powerful ML models on devices by taking full advantage of the unified representation for all models. It essentially includes – TensorFlow, PyTorch, Keras, LibSVM, dmlc XGBoost, ONNX, Caffe, and PyTorch.

Challenges: Is facial recognition an ethical issue?

Face recognition technologies are often implemented without consent or notification. Having access to surveillance cameras or video feeds of employees, the general public, or customers does not mean that it’s a good idea to use that data without informing the affected parties. The use of Facial Recognition Technology (FRT) often poses a significant security threat to its users because it makes use of biometric data (facial images), which can easily exploit identity theft and other malicious purposes. 

Monetization: How do doppelganger apps make money?

  • Advertising through banner placements, short advertising, full-screen advertising, and providing ads with gamification elements
  • Subscription: By subscribing to videos, tutorials, videos, and cloud services leading the market.
  • In-app purchases: It offers users to see the actual value of the paid content.
  • Sponsorship: If your app receives daily user traffic, the app publishers can connect with other businesses with the same business niche, equally divide the total revenue, and chalk out the monthly sponsorship fees.

Conclusion:

Facial recognition technology studies are based on the representation stage but it is also important to determine the architecture for production-driven applications. Most of the technology stacks for FRT have passed the human-level accuracy.

The facial recognition market generated 5 billion dollars (US) in 2021. And this market is projected at 12.67 billion USD by 2028. FRT inclusive of AI is used to identify a person by reading their facial features.

This technology has also been used to aid in research beyond the creation of art. But consumers have resisted it often over concerns of privacy. There is a rational debate on whether the perceived benefits of facial recognition are worth the negative impacts it may have on marginalized groups and a population’s overall privacy. Some focused artists have even created means of avoiding facial detection by tricking the recognition algorithm. But this method is imperfect and can be difficult to implement correctly.


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